Systems, apparatus, and methods for adjusting parameters in response to anticipated component state

[0001] This application claims the benefit of, and is a continuation of, U.S. patent application Ser. No. 18/074,361, filed Dec. 2, 2022, entitled “SYSTEMS FOR SELF-ORGANIZING DATA COLLECTION AND STORAGE IN A MANUFACTURING ENVIRONMENT” and published Apr. 6, 2023, as U.S. Patent Application Publication No. 2023/0109195.

[0002] U.S. patent application Ser. No. 18/074,361 claims the benefit of, and is a continuation of, U.S. Non-Provisional patent application Ser. No. 16/698,606, filed Nov. 27, 2019, entitled SYSTEMS AND METHODS FOR ADJUSTING PROCESS PARAMETERS IN A PRODUCTION ENVIRONMENT, issued Feb. 21, 2023, as U.S. Pat. No. 11,586,181.

[0003] U.S. patent application Ser. No. 16/698,606 claims the benefit of, and is a continuation of, U.S. Non-Provisional patent application Ser. No. 16/143,286, filed Sep. 26, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH FREQUENCY BAND ADJUSTMENTS FOR DIAGNOSING OIL AND GAS PRODUCTION EQUIPMENT, issued Jun. 8, 2021, as U.S. Pat. No. 11,029,680.

[0004] U.S. patent application Ser. No. 16/143,286 is a continuation of U.S. Non-Provisional patent application Ser. No. 15/973,406, filed May 7, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS and published Oct. 4, 2018, as U.S. Patent Application Publication No. 2018/0284737.

[0005] U.S. Ser. No. 15/973,406 is a bypass continuation-in-part of International Application Number PCT/US17/31721, filed May 9, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS, published on Nov. 16, 2017, as WO 2017/196821, which claims priority to: U.S. Provisional Patent Application Ser. No. 62/333,589, filed May 9, 2016, entitled STRONG FORCE INDUSTRIAL IOT MATRIX; U.S. Provisional Patent Application Ser. No. 62/350,672, filed Jun. 15, 2016, entitled STRATEGY FOR HIGH SAMPLING RATE DIGITAL RECORDING OF MEASUREMENT WAVEFORM DATA AS PART OF AN AUTOMATED SEQUENTIAL LIST THAT STREAMS LONG-DURATION AND GAP-FREE WAVEFORM DATA TO STORAGE FOR MORE FLEXIBLE POST-PROCESSING; U.S. Provisional Patent Application Ser. No. 62/412,843, filed Oct. 26, 2016, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS; and U.S. Provisional Patent Application Ser. No. 62/427,141, filed Nov. 28, 2016, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS.

[0006] U.S. Ser. No. 16/143,286 claims the benefit of, and is a bypass continuation of, International Application Number PCT/US18/45036, filed Aug. 2, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS, published Oct. 4, 2018 as U.S. Patent Application Publication No. 2018/0284737.

[0007] International Application Number PCT/US18/45036 claims priority to: U.S. Provisional Patent Application Ser. No. 62/540,557, filed Aug. 2, 2017, entitled SMART HEATING SYSTEMS IN AN INDUSTRIAL INTERNET OF THINGS; U.S. Provisional Patent Application Ser. No. 62/540,513, filed Aug. 2, 2017, entitled SYSTEMS AND METHODS FOR SMART HEATING SYSTEM THAT PRODUCES AND USES HYDROGEN FUEL; U.S. Provisional Patent Application Ser. No. 62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS; and U.S. Provisional Patent Application Ser. No. 62/583,487, filed Nov. 8, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS.

[0008] All of the foregoing applications are hereby incorporated by reference as if fully set forth herein in their entirety.

[0009] The present disclosure relates to methods and systems for data collection in industrial environments, as well as methods and systems for leveraging collected data for monitoring, remote control, autonomous action, and other activities in industrial environments.

[0010] Heavy industrial environments, such as environments for large scale manufacturing (such as manufacturing of aircraft, ships, trucks, automobiles, and large industrial machines), energy production environments (such as oil and gas plants, renewable energy environments, and others), energy extraction environments (such as mining, drilling, and the like), construction environments (such as for construction of large buildings), and others, involve highly complex machines, devices and systems and highly complex workflows, in which operators must account for a host of parameters, metrics, and the like in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results. Historically, data has been collected in heavy industrial environments by human beings using dedicated data collectors, often recording batches of specific sensor data on media, such as tape or a hard drive, for later analysis. Batches of data have historically been returned to a central office for analysis, such as undertaking signal processing or other analysis on the data collected by various sensors, after which analysis can be used as a basis for diagnosing problems in an environment and/or suggesting ways to improve operations. This work has historically taken place on a time scale of weeks or months, and has been directed to limited data sets.

[0011] The emergence of the Internet of Things (IoT) has made it possible to connect continuously to, and among, a much wider range of devices. Most such devices are consumer devices, such as lights, thermostats, and the like. More complex industrial environments remain more difficult, as the range of available data is often limited, and the complexity of dealing with data from multiple sensors makes it much more difficult to produce “smart” solutions that are effective for the industrial sector. A need exists for improved methods and systems for data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments.

[0012] Industrial system in various environments have a number of challenges to utilizing data from a multiplicity of sensors. Many industrial systems have a wide range of computing resources and network capabilities at a location at a given time, for example as parts of the system are upgraded or replaced on varying time scales, as mobile equipment enters or leaves a location, and due to the capital costs and risks of upgrading equipment. Additionally, many industrial systems are positioned in challenging environments, where network connectivity can be variable, where a number of noise sources such as vibrational noise and electro-magnetic (EM) noise sources can be significant in varied locations, and with portions of the system having high pressure, high noise, high temperature, and corrosive materials. Many industrial processes are subject to high variability in process operating parameters and non-linear responses to off-nominal operations. Accordingly, sensing requirements for industrial processes can vary with time, operating stages of a process, age and degradation of equipment, and operating conditions. Previously known industrial processes suffer from sensing configurations that are conservative, detecting many parameters that are not needed during most operations of the industrial system, or that accept risk in the process, and do not detect parameters that are only occasionally utilized in characterizing the system. Further, previously known industrial systems are not flexible to configuring sensed parameters rapidly and in real-time, and in managing system variance such as intermittent network availability. Industrial systems often use similar components across systems such as pumps, mixers, tanks, and fans. However, previously known industrial systems do not have a mechanism to leverage data from similar components that may be used in a different type of process, and/or that may be unavailable due to competitive concerns. Additionally, previously known industrial systems do not integrate data from offset systems into the sensor plan and execution in real time.

[0013] The present disclosure describes a system for process monitoring through data collection in a production environment including a data collector communicatively coupled to a plurality of input channels, each input channel connected to a monitoring point from which data is collected, the collected data providing a plurality of process parameter values for the production environment; a data storage structured to store collected data from the plurality of input channels; a data acquisition circuit structured to interpret the plurality of process parameter values from the collected data; a data analysis circuit structured to analyze the plurality of process parameter values to detect a process condition associated with the production environment; and a response circuit structured to adjust an operational process for the production environment in response to the detected process condition. The operational process may be a rotational rate of a component in the production environment. The operational process parameter may be at least one of a production outcome, a product quality description, a product yield description, a product quantity description, a product variability description, a process completion time, a product purity result, or a production capacity. The data storage may further store a plurality of collector routes, wherein the plurality of collector routes each comprise a different data collection routine, wherein a selected collector route is switched from a first collector route to a second collector route based on the analysis of the plurality of process parameter values. The switched collector route may be due to the data analysis circuit detecting a change in an operating stage of the production environment. The monitoring point may provide a continuously monitored alarm having a pre-determined trigger condition, and the data analysis circuit detects the pre-determined trigger condition. The response circuit may be further structured to adjust the operational process by performing at least one of: changing an equipment type, changing operating parameters for a piece of equipment, initiating amelioration of an equipment issue, or making recommendations regarding future equipment for a system. The operational process may be altered to increase productivity of the production environment. The data analysis circuit may utilize a neural network to analyze the plurality of process parameter values. The neural network may be a probabilistic neural network to predict the process condition as a fault condition. The neural network may be a convolutional neural network to make a recommendation based on the analysis of the plurality of process parameter values. The neural network may be switched between a first neural network to a second neural network by an expert system. The data analysis circuit may compare the plurality of process parameter values to a stored vibration fingerprint to detect the process condition. The data analysis circuit may utilize a noise pattern analysis to detect the process condition.

[0014] The present disclosure describes a computer-implemented method for process monitoring through data collection in a production environment including collecting data from a data collector communicatively coupled to a plurality of input channels, each input channel connected to a monitoring point from which data is collected, the collected data providing a plurality of process parameter values for the production environment; interpreting the plurality of process parameter values from the collected data; analyzing the plurality of process parameter values to detect a process condition associated with the production environment; and adjusting an operational process for the production environment in response to the detected process condition. The operational process may be a rotational rate of a component in the production environment. The computer-implemented method may further include storing a plurality of collector routes in a data storage, wherein the plurality of collector routes each comprise a different data collection routine, wherein the collector route is switched from a first collector route to a second collector route based on the analysis of the plurality of process parameter values.

[0015] The present disclosure describes an apparatus for process monitoring through data collection in a production environment including a data collector component communicatively coupled to a plurality of input channels, each input channel connected to a monitoring point from which data is collected, the collected data providing a plurality of process parameter values for the production environment; a data storage component structured to store collected data from the plurality of input channels; a data acquisition component structured to interpret the plurality of process parameter values from the collected data; a data analysis component structured to analyze the plurality of process parameter values to detect a process condition associated with the production environment; and a response component structured to adjust an operational process for the production environment in response to a detected process condition. The operational process may be a rotational rate of a component in the production environment. The data storage component may further store a plurality of collector routes, wherein the plurality of collector routes each comprise a different data collection routine, wherein the collector route is switched from a first collector route to a second collector route based on the analysis of the plurality of process parameter values.

[0016] The present disclosure describes a method of data collection in an industrial environment, the method according to one disclosed non-limiting embodiment of the present disclosure can include: routing analog signals from a plurality of analog sensor inputs to a plurality of output channels of a data collector in accordance with a first routing scheme, wherein the first routing scheme includes providing at least two of the plurality of analog sensor inputs at one of the plurality of output channels; adjusting the routing scheme to a second routing scheme, wherein the second routing scheme includes one of: providing at least one of the at least two of the plurality of analog sensor inputs to a different one of the plurality of output channels, or stopping providing the at least one of the at least two of the plurality of analog sensor inputs at the one of the plurality of output channels; and routing the analog signals from the plurality of analog sensor inputs to the plurality of output channels of the data collector in accordance with the second routing scheme after adjusting the routing scheme.

[0017] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the data collector includes an analog crosspoint switch capable of providing any of the plurality of analog sensor inputs to any one or more of the output channels of the data collector, and wherein the adjusting the routing scheme includes adjusting a switching of the analog crosspoint switch.

[0018] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes adjusting the routing scheme by providing an electronic signal to the analog crosspoint switch.

[0019] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the analog sensor inputs include a plurality of analog sensors that detect a condition of the industrial environment and provide the plurality of analog sensor inputs that include an analog signal representative of the condition.

[0020] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the analog crosspoint switch preserves at least one aspect of the analog signal.

[0021] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes buffering at least one of the plurality of analog sensor inputs, and wherein providing the at least one of the plurality of analog sensor inputs at one of the plurality of output channels includes using the buffered at least one of the plurality of analog sensor inputs.

[0022] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein buffering further includes at least one of: filtering the one of the plurality of analog inputs, debouncing the one of the plurality of analog inputs, limiting a range of the one of the plurality of analog inputs, processing the one of the plurality of analog inputs, performing a short-term storage of the one of the plurality of analog inputs, adjusting a communication rate of the one of the plurality of analog inputs, and electrically isolating the one of the plurality of analog inputs from the plurality of output channels.

[0023] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes using a relay to propagate one of the analog sensor inputs to at least one of the plurality of output channels.

[0024] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes performing at least one operation selected from the operations consisting of: communicating a digital signal representative of the one of the analog sensor inputs at the one of the plurality of output channels; reducing an energy consumption of communication operations of the one of the analog sensor inputs; detecting an aberrant condition indicated by the one of the analog sensor inputs before the providing the one of the analog sensor inputs to the one of the plurality of output channels; and processing the one of the analog sensor inputs before the providing the one of the analog sensor inputs to the one of the plurality of output channels.

[0025] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes storing a first portion of the plurality of analog sensor inputs, and communicating a second portion of the plurality of analog sensor inputs to the plurality of output channels.

[0026] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes determining that a data collection network has available bandwidth, and providing the first portion of the plurality of analog sensor inputs to the plurality of output channels in response to the data collection network having available bandwidth.

[0027] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes providing the first portion of the plurality of analog sensor inputs with a time stamp indicating a collection time of the first portion of the plurality of analog sensor inputs.

[0028] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the method further includes determining at least one of a data collection priority value or a data validity value corresponding to at least one of the plurality of analog sensor inputs, and providing the at least one of the plurality of analog sensor inputs to at least one of the plurality of output channels in response to the determining the at least one of the data collection priority value or the data validity value.

[0029] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein a signal source provides a data valid signal that transmits an indication of when a corresponding one of the analog sensor inputs is available.

[0030] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein each of the first routing scheme and the second routing scheme include a signal route plan.

[0031] The present disclosure describes a system for data collection in an industrial environment, the system according to one disclosed non-limiting embodiment of the present disclosure can include a data collector configured to route analog signals from a plurality of analog sensor inputs to a plurality of output channels of a data collector in accordance with a first routing scheme, wherein the first routing scheme includes providing at least two of the plurality of analog sensor inputs at one of the plurality of output channels; a controller configured to adjust the routing scheme to a second routing scheme, wherein the second routing scheme includes one of: providing at least one of the at least two of the plurality of analog sensor inputs to a different one of the plurality of output channels, or stopping providing the at least one of the at least two of the plurality of analog sensor inputs at the one of the plurality of output channels; and wherein the controller is further configured to route the analog signals from the plurality of analog sensor inputs to the plurality of output channels of the data collector in accordance with the second routing scheme after adjusting the routing scheme.

[0032] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the data collector includes an analog crosspoint switch capable of providing any of the plurality of analog sensor inputs to any one or more of the output channels of the data collector, and wherein the adjusting the routing scheme includes adjusting a switching of the analog crosspoint switch.

[0033] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the system further includes an analog-to-digital (A/D) converter circuit for processing one of the analog sensor inputs before providing the one of the analog sensor inputs to one of the plurality of output channels.

[0034] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the analog crosspoint switch has more input channels than output channels.

[0035] A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the analog crosspoint switch has more output channels than input channels.

[0036] In an aspect, systems for monitoring data collection in an industrial environment may include a data collector communicatively coupled to a plurality of input channels connected to data collection points operatively coupled to at least one of an oil production component or gas production component; a data storage structured to store a plurality of diagnostic frequency band ranges for the at least one of an oil production component or gas production component; a data acquisition circuit structured to interpret a plurality of detection values from the plurality of input channels; and a data analysis circuit structured to analyze the plurality of detection values to determine measured frequency band data and compare the measured frequency band data to the plurality of diagnostic frequency band ranges, and to diagnose an operational parameter of the least one of an oil production component or gas production component in response to the comparison. In embodiments, the plurality of diagnostic frequency band ranges may include a gap-free digital waveform, and wherein the operational parameter comprises an anomalous condition of the at least one of the oil production component or gas production component. An expert circuit structured to operate one of a machine-learning or expert system may be provided to compare the measured frequency band data to the plurality of diagnostic frequency band ranges. The one of the machine-learning or expert system may interpret diagnostic frequency band ranges from an external data source. The one of a machine-learning or expert system may be configured to provide at least a portion of the plurality of diagnostic frequency band ranges to a self-organizing marketplace. A graphical user interface may be provided to manage the stored plurality of diagnostic frequency band ranges. The stored plurality of diagnostic frequency band ranges may include accepting a user selection of diagnostic frequency band ranges for detecting off-nominal operations. The measured frequency band data may be determined utilizing a band pass tracking filter, wherein a machine learning system uses the band pass tracking filter to learn a frequency band of interest over time, and wherein the data analysis circuit is further structured to diagnose the operational parameter in response to the learned frequency band of interest over time. A response circuit may provide a haptic notification in response to the operational parameter indicating an anomalous operating condition.

[0037] In an aspect, a computer-implemented method for data collection in an industrial environment may include collecting data with a data collector communicatively coupled to a plurality of input channels connected to data collection points operatively coupled to at least one of an oil production component or gas production component; storing a plurality of diagnostic frequency band ranges for the at least one of the oil production component or gas production component; interpreting a plurality of detection values from the plurality of input channels; and analyzing the plurality of detection values to determine measured frequency band data and comparing the measured frequency band data to the plurality of diagnostic frequency band ranges, and diagnosing an operational parameter of the least one of the oil production component or gas production component in response to the comparing. In embodiments, the plurality of diagnostic frequency band ranges may include a gap-free digital waveform, wherein the operational parameter comprises an anomalous condition of the at least one of the oil production component or gas production component. The diagnostic frequency band ranges may be interpreted from an external data source. The measured frequency band data may be determined utilizing a band pass tracking filter, operating a machine learning system using the band pass tracking filter to learn a frequency band of interest over time, and wherein diagnosing the operational parameter is further in response to the learned frequency band of interest over time.

[0038] In an aspect, an apparatus for monitoring data collection in an industrial environment may include a data collector communicatively coupled to a plurality of input channels connected to data collection points operatively coupled to at least one of an oil production component or gas production component; a data storage structured to store a plurality of diagnostic frequency band ranges for the at least one of an oil production component or gas production component; a data acquisition circuit structured to interpret a plurality of detection values from the plurality of input channels; and a data analysis circuit structured to analyze the plurality of detection values to determine measured frequency band data and compare the measured frequency band data to the plurality of diagnostic frequency band ranges, and to diagnose the at least one of an oil production component or gas production component in response to the comparison. In embodiments, the data analysis circuit may be further structured to diagnose at least one operational parameter of the at least one of an oil production component or gas production component selected from the parameters consisting of: a failure parameter, a fault parameter, an off-nominal operating condition, a saturated operating condition, a predicted failure operating condition, a component change operating condition, and a maintenance indication for the component. The plurality of diagnostic frequency band ranges may include a gap-free digital waveform for the at least one of an oil production component or gas production component. An expert circuit structured may be provided to operate one of a machine-learning or expert system to compare the measured frequency band data to the plurality of diagnostic frequency band ranges. The one of a machine-learning or expert system may interpret the diagnostic frequency band ranges from an external data source. The one of a machine-learning or expert system may be configured to provide at least a portion of the plurality of diagnostic frequency band ranges to a self-organizing marketplace. A graphical user interface may be provided to manage the stored plurality of diagnostic frequency band ranges.

[0039] Methods and systems are provided herein for data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments. These methods and systems include methods, systems, components, devices, workflows, services, processes, and the like that are deployed in various configurations and locations, such as: (a) at the “edge” of the Internet of Things, such as in the local environment of a heavy industrial machine; (b) in data transport networks that move data between local environments of heavy industrial machines and other environments, such as of other machines or of remote controllers, such as enterprises that own or operate the machines or the facilities in which the machines are operated; and (c) in locations where facilities are deployed to control machines or their environments, such as cloud-computing environments and on-premises computing environments of enterprises that own or control heavy industrial environments or the machines, devices or systems deployed in them. These methods and systems include a range of ways for providing improved data include a range of methods and systems for providing improved data collection, as well as methods and systems for deploying increased intelligence at the edge, in the network, and in the cloud or premises of the controller of an industrial environment.

[0040] Methods and systems are disclosed herein for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility; for cloud-based systems including machine pattern recognition based on the fusion of remote, analog industrial sensors or machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system; for on-device sensor fusion and data storage for industrial IoT devices, including on-device sensor fusion and data storage for an Industrial IoT device, where data from multiple sensors are multiplexed at the device for storage of a fused data stream; and for self-organizing systems including a self-organizing data marketplace for industrial IoT data, including a self-organizing data marketplace for industrial IoT data, where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success, for self-organizing data pools, including self-organization of data pools based on utilization and/or yield metrics, including utilization and/or yield metrics that are tracked for a plurality of data pools, a self-organized swarm of industrial data collectors, including a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm, a self-organizing collector, including a self-organizing, multi-sensor data collector that can optimize data collection, power and/or yield based on conditions in its environment, a self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data, a self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment.

[0041] Methods and systems are disclosed herein for training artificial intelligence (“AI”) models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, where the AI model operates on sensor data from an industrial environment; for an industrial IoT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial IoT data; for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing, and/or other network conditions; for a remotely organized universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment; and for a haptic or multi-sensory user interface, including a wearable haptic or multi-sensory user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs.

[0042] Methods and systems are disclosed herein for a presentation layer for augmented reality and virtual reality (AR/VR) industrial glasses, where heat map elements are presented based on patterns and/or parameters in collected data; and for condition-sensitive, self-organized tuning of AR/VR interfaces based on feedback metrics and/or training in industrial environments.

[0043] In embodiments, a system for data collection, processing, and utilization of signals from at least a first element in a first machine in an industrial environment includes a platform including a computing environment connected to a local data collection system having at least a first sensor signal and a second sensor signal obtained from at least the first machine in the industrial environment. The system includes a first sensor in the local data collection system configured to be connected to the first machine and a second sensor in the local data collection system. The system further includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor. Throughout the present disclosure, wherever a crosspoint switch, multiplexer (MUX) device, or other multiple-input multiple-output data collection or communication device is described, any multi-sensor acquisition device is also contemplated herein. In certain embodiments, a multi-sensor acquisition device includes one or more channels configured for, or compatible with, an analog sensor input. The multiple outputs include a first output and second output configured to be switchable between a condition in which the first output is configured to switch between delivery of the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs, or combined in any subsets of the inputs to the outputs. Unassigned outputs are configured to be switched off, for example by producing a high-impedance state.

[0044] In embodiments, the first sensor signal and the second sensor signal are continuous vibration data about the industrial environment. In embodiments, the second sensor in the local data collection system is configured to be connected to the first machine. In embodiments, the second sensor in the local data collection system is configured to be connected to a second machine in the industrial environment. In embodiments, the computing environment of the platform is configured to compare relative phases of the first and second sensor signals. In embodiments, the first sensor is a single-axis sensor and the second sensor is a three-axis sensor. In embodiments, at least one of the multiple inputs of the crosspoint switch includes internet protocol, front-end signal conditioning, for improved signal-to-noise ratio. In embodiments, the crosspoint switch includes a third input that is configured with a continuously monitored alarm having a pre-determined trigger condition when the third input is unassigned to or undetected at any of the multiple outputs.

[0045] In embodiments, the local data collection system includes multiple multiplexing units and multiple data acquisition units receiving multiple data streams from multiple machines in the industrial environment. In embodiments, the local data collection system includes distributed complex programmable hardware device (“CPLD”) chips each dedicated to a data bus for logic control of the multiple multiplexing units and the multiple data acquisition units that receive the multiple data streams from the multiple machines in the industrial environment. In embodiments, the local data collection system is configured to provide high-amperage input capability using solid state relays. In embodiments, the local data collection system is configured to power-down at least one of an analog sensor channel and a component board.

[0046] In embodiments, the local data collection system includes a phase-lock loop band-pass tracking filter configured to obtain slow-speed revolutions per minute (“RPMs”) and phase information. In embodiments, the local data collection system is configured to digitally derive phase using on-board timers relative to at least one trigger channel and at least one of the multiple inputs. In embodiments, the local data collection system includes a peak-detector configured to autoscale using a separate analog-to-digital converter for peak detection. In embodiments, the local data collection system is configured to route at least one trigger channel that is raw and buffered into at least one of the multiple inputs. In embodiments, the local data collection system includes at least one delta-sigma analog-to-digital converter that is configured to increase input oversampling rates to reduce sampling rate outputs and to minimize anti-aliasing filter requirements. In embodiments, the distributed CPLD chips each dedicated to the data bus for logic control of the multiple multiplexing units and the multiple data acquisition units includes as high-frequency crystal clock reference configured to be divided by at least one of the distributed CPLD chips for at least one delta-sigma analog-to-digital converter to achieve lower sampling rates without digital resampling.

[0047] In embodiments, the local data collection system is configured to obtain long blocks of data at a single relatively high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, the single relatively high-sampling rate corresponds to a maximum frequency of about forty kilohertz. In embodiments, the long blocks of data are for a duration that is in excess of one minute. In embodiments, the local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history of a data acquisition unit in which the onboard card set is located. In embodiments, the local data collection system is configured to plan data acquisition routes based on hierarchical templates.

[0048] In embodiments, the local data collection system is configured to manage data collection bands. In embodiments, the data collection bands define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. In embodiments, the local data collection system includes a neural net expert system using intelligent management of the data collection bands. In embodiments, the local data collection system is configured to create data acquisition routes based on hierarchical templates that each include the data collection bands related to machines associated with the data acquisition routes. In embodiments, at least one of the hierarchical templates is associated with multiple interconnected elements of the first machine. In embodiments, at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine.

[0049] In embodiments, the local data collection system includes a graphical user interface (“GUI”) system configured to manage the data collection bands. In embodiments, the GUI system includes an expert system diagnostic tool. In embodiments, the platform includes cloud-based, machine pattern analysis of state information from multiple sensors to provide anticipated state information for the industrial environment. In embodiments, the platform is configured to provide self-organization of data pools based on at least one of the utilization metrics and yield metrics. In embodiments, the platform includes a self-organized swarm of industrial data collectors. In embodiments, the local data collection system includes a wearable haptic user interface for an industrial sensor data collector with at least one of vibration, heat, electrical, and sound outputs.

[0050] In embodiments, multiple inputs of the crosspoint switch include a third input connected to the second sensor and a fourth input connected to the second sensor. The first sensor signal is from a single-axis sensor at an unchanging location associated with the first machine. In embodiments, the second sensor is a three-axis sensor. In embodiments, the local data collection system is configured to record gap-free digital waveform data simultaneously from at least the first input, the second input, the third input, and the fourth input. In embodiments, the platform is configured to determine a change in relative phase based on the simultaneously recorded gap-free digital waveform data. In embodiments, the second sensor is configured to be movable to a plurality of positions associated with the first machine while obtaining the simultaneously recorded gap-free digital waveform data. In embodiments, multiple outputs of the crosspoint switch include a third output and fourth output. The second, third, and fourth outputs are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the platform is configured to determine an operating deflection shape based on the change in relative phase and the simultaneously recorded gap-free digital waveform data.

[0051] In embodiments, the unchanging location is a position associated with the rotating shaft of the first machine. In embodiments, tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions on the first machine but are each associated with different bearings in the machine. In embodiments, tri-axial sensors in the sequence of the tri-axial sensors are each located at similar positions associated with similar bearings but are each associated with different machines. In embodiments, the local data collection system is configured to obtain the simultaneously recorded gap-free digital waveform data from the first machine while the first machine and a second machine are both in operation. In embodiments, the local data collection system is configured to characterize a contribution from the first machine and the second machine in the simultaneously recorded gap-free digital waveform data from the first machine. In embodiments, the simultaneously recorded gap-free digital waveform data has a duration that is in excess of one minute.

[0052] In embodiments, a method of monitoring a machine having at least one shaft supported by a set of bearings includes monitoring a first data channel assigned to a single-axis sensor at an unchanging location associated with the machine. The method includes monitoring second, third, and fourth data channels each assigned to an axis of a three-axis sensor. The method includes recording gap-free digital waveform data simultaneously from all of the data channels while the machine is in operation and determining a change in relative phase based on the digital waveform data.

[0053] In embodiments, the tri-axial sensor is located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors simultaneously. In embodiments, the method includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location is a position associated with the shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with the shaft of the machine. The tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine.

[0054] In embodiments, the method includes monitoring the first data channel assigned to the single-axis sensor at an unchanging location located on a second machine. The method includes monitoring the second, the third, and the fourth data channels, each assigned to the axis of a three-axis sensor that is located at the position associated with the second machine. The method also includes recording gap-free digital waveform data simultaneously from all of the data channels from the second machine while both of the machines are in operation. In embodiments, the method includes characterizing the contribution from each of the machines in the gap-free digital waveform data simultaneously from the second machine.

[0055] In embodiments, a method for data collection, processing, and utilization of signals with a platform monitoring at least a first element in a first machine in an industrial environment includes obtaining, automatically with a computing environment, at least a first sensor signal and a second sensor signal with a local data collection system that monitors at least the first machine. The method includes connecting a first input of a crosspoint switch of the local data collection system to a first sensor and a second input of the crosspoint switch to a second sensor in the local data collection system. The method includes switching between a condition in which a first output of the crosspoint switch alternates between delivery of at least the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from a second output of the crosspoint switch. The method also includes switching off unassigned outputs of the crosspoint switch into a high-impedance state.

[0056] In embodiments, the first sensor signal and the second sensor signal are continuous vibration data from the industrial environment. In embodiments, the second sensor in the local data collection system is connected to the first machine. In embodiments, the second sensor in the local data collection system is connected to a second machine in the industrial environment. In embodiments, the method includes comparing, automatically with the computing environment, relative phases of the first and second sensor signals. In embodiments, the first sensor is a single-axis sensor and the second sensor is a three-axis sensor. In embodiments, at least the first input of the crosspoint switch includes internet protocol front-end signal conditioning for improved signal-to-noise ratio.

[0057] In embodiments, the method includes continuously monitoring at least a third input of the crosspoint switch with an alarm having a pre-determined trigger condition when the third input is unassigned to any of multiple outputs on the crosspoint switch. In embodiments, the local data collection system includes multiple multiplexing units and multiple data acquisition units receiving multiple data streams from multiple machines in the industrial environment. In embodiments, the local data collection system includes distributed CPLD chips each dedicated to a data bus for logic control of the multiple multiplexing units and the multiple data acquisition units that receive the multiple data streams from the multiple machines in the industrial environment. In embodiments, the local data collection system provides high-amperage input capability using solid state relays.

[0058] In embodiments, the method includes powering down at least one of an analog sensor channel and a component board of the local data collection system. In embodiments, the local data collection system includes an external voltage reference for an A/D zero reference that is independent of the voltage of the first sensor and the second sensor. In embodiments, the local data collection system includes a phase-lock loop band-pass tracking filter that obtains slow-speed RPMs and phase information. In embodiments, the method includes digitally deriving phase using on-board timers relative to at least one trigger channel and at least one of multiple inputs on the crosspoint switch.

[0059] In embodiments, the method includes auto-scaling with a peak-detector using a separate analog-to-digital converter for peak detection. In embodiments, the method includes routing at least one trigger channel that is raw and buffered into at least one of multiple inputs on the crosspoint switch. In embodiments, the method includes increasing input oversampling rates with at least one delta-sigma analog-to-digital converter to reduce sampling rate outputs and to minimize anti-aliasing filter requirements. In embodiments, the distributed CPLD chips are each dedicated to the data bus for logic control of the multiple multiplexing units and the multiple data acquisition units and each include a high-frequency crystal clock reference divided by at least one of the distributed CPLD chips for at least one delta-sigma analog-to-digital converter to achieve lower sampling rates without digital resampling. In embodiments, the method includes obtaining long blocks of data at a single relatively high-sampling rate with the local data collection system as opposed to multiple sets of data taken at different sampling rates. In embodiments, the single relatively high-sampling rate corresponds to a maximum frequency of about forty kilohertz. In embodiments, the long blocks of data are for a duration that is in excess of one minute. In embodiments, the local data collection system includes multiple data acquisition units and each data acquisition unit has an onboard card set that stores calibration information and maintenance history of a data acquisition unit in which the onboard card set is located.

[0060] In embodiments, the method includes planning data acquisition routes based on hierarchical templates associated with at least the first element in the first machine in the industrial environment. In embodiments, the local data collection system manages data collection bands that define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. In embodiments, the local data collection system includes a neural net expert system using intelligent management of the data collection bands. In embodiments, the local data collection system creates data acquisition routes based on hierarchical templates that each include the data collection bands related to machines associated with the data acquisition routes. In embodiments, at least one of the hierarchical templates is associated with multiple interconnected elements of the first machine. In embodiments, at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine.

[0061] In embodiments, the method includes controlling a GUI system of the local data collection system to manage the data collection bands. The GUI system includes an expert system diagnostic tool. In embodiments, the computing environment of the platform includes cloud-based, machine pattern analysis of state information from multiple sensors to provide anticipated state information for the industrial environment. In embodiments, the computing environment of the platform provides self-organization of data pools based on at least one of the utilization metrics and yield metrics. In embodiments, the computing environment of the platform includes a self-organized swarm of industrial data collectors. In embodiments, each of multiple inputs of the crosspoint switch is individually assignable to any of multiple outputs of the crosspoint switch.

[0062] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for capturing a plurality of streams of sensed data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine; at least one of the streams contains a plurality of frequencies of data. The method may include identifying a subset of data in at least one of the plurality of streams that corresponds to data representing at least one predefined frequency. The at least one predefined frequency is represented by a set of data collected from alternate sensors deployed to monitor aspects of the industrial machine associated with the at least one moving part of the machine. The method may further include processing the identified data with a data processing facility that processes the identified data with an algorithm configured to be applied to the set of data collected from alternate sensors. Lastly, the method may include storing the at least one of the streams of data, the identified subset of data, and a result of processing the identified data in an electronic data set.

[0063] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a method for applying data captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. The data is captured with predefined lines of resolution covering a predefined frequency range and is sent to a frequency matching facility that identifies a subset of data streamed from other sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine. The streamed data includes a plurality of lines of resolution and frequency ranges. The subset of data identified corresponds to the lines of resolution and predefined frequency range. This method may include storing the subset of data in an electronic data record in a format that corresponds to a format of the data captured with predefined lines of resolution and signaling to a data processing facility the presence of the stored subset of data. This method may, optionally, include processing the subset of data with at least one set of algorithms, models and pattern recognizers that corresponds to algorithms, models and pattern recognizers associated with processing the data captured with predefined lines of resolution covering a predefined frequency range.

[0064] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for identifying a subset of streamed sensor data, the sensor data captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the subset of streamed sensor data at predefined lines of resolution for a predefined frequency range, and establishing a first logical route for communicating electronically between a first computing facility performing the identifying and a second computing facility, wherein identified subset of the streamed sensor data is communicated exclusively over the established first logical route when communicating the subset of streamed sensor data from the first facility to the second facility. This method may further include establishing a second logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that is not the identified subset. Additionally, this method may further include establishing a third logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that includes the identified subset and at least one other portion of the data not represented by the identified subset.

[0065] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a first data sensing and processing system that captures first data from a first set of sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the first data covering a set of lines of resolution and a frequency range. This system may include a second data sensing and processing system that captures and streams a second set of data from a second set of sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine, the second data covering a plurality of lines of resolution that includes the set of lines of resolution and a plurality of frequencies that includes the frequency range. The system may enable selecting a portion of the second data that corresponds to the set of lines of resolution and the frequency range of the first data, and processing the selected portion of the second data with the first data sensing and processing system.

[0066] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for automatically processing a portion of a stream of sensed data. The sensed data is received from a first set of sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. The sensed data is in response to an electronic data structure that facilitates extracting a subset of the stream of sensed data that corresponds to a set of sensed data received from a second set of sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The set of sensed data is constrained to a frequency range. The stream of sensed data includes a range of frequencies that exceeds the frequency range of the set of sensed data, the processing comprising executing an algorithm on a portion of the stream of sensed data that is constrained to the frequency range of the set of sensed data, the algorithm configured to process the set of sensed data.

[0067] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for receiving first data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. This method may further include detecting at least one of a frequency range and lines of resolution represented by the first data; receiving a stream of data from sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The stream of data includes: (1) a plurality of frequency ranges and a plurality of lines of resolution that exceeds the frequency range and the lines of resolution represented by the first data; (2) a set of data extracted from the stream of data that corresponds to at least one of the frequency range and the lines of resolution represented by the first data; and (3) the extracted set of data which is processed with a data processing algorithm that is configured to process data within the frequency range and within the lines of resolution of the first data.

[0231] Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.

[0232] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate with existing data collection, processing, and storage systems while preserving access to existing format/frequency range/resolution compatible data. While the industrial machine sensor data streaming facilities described herein may collect a greater volume of data (e.g., longer duration of data collection) from sensors at a wider range of frequencies and with greater resolution than existing data collection systems, methods and systems may be employed to provide access to data from the stream of data that represents one or more ranges of frequency and/or one or more lines of resolution that are purposely compatible with existing systems. Further, a portion of the streamed data may be identified, extracted, stored, and/or forwarded to existing data processing systems to facilitate operation of existing data processing systems that substantively matches operation of existing data processing systems using existing collection-based data. In this way, a newly deployed system for sensing aspects of industrial machines, such as aspects of moving parts of industrial machines, may facilitate continued use of existing sensed data processing facilities, algorithms, models, pattern recognizers, user interfaces, and the like.

[0233] Through identification of existing frequency ranges, formats, and/or resolution, such as by accessing a data structure that defines these aspects of existing data, higher resolution streamed data may be configured to represent a specific frequency, frequency range, format, and/or resolution. This configured streamed data can be stored in a data structure that is compatible with existing sensed data structures so that existing processing systems and facilities can access and process the data substantially as if it were the existing data. One approach to adapting streamed data for compatibility with existing sensed data may include aligning the streamed data with existing data so that portions of the streamed data that align with the existing data can be extracted, stored, and made available for processing with existing data processing methods. Alternatively, data processing methods may be configured to process portions of the streamed data that correspond, such as through alignment, to the existing data, with methods that implement functions substantially similar to the methods used to process existing data, such as methods that process data that contain a particular frequency range or a particular resolution and the like.

[0234] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range. This method can thusly be at least partially identifiable by these characteristics of the data being processed. Therefore, given a set of conditions, such as moving device being sensed, industrial machine type, frequency of data being sensed, and the like, a data processing system may select an appropriate method. Also, given such a set of conditions, an industrial machine data sensing and processing facility may configure elements, such as data filters, routers, processors, and the like, to handle data meeting the conditions.

[0235] FIGS. 1 through 5 depict portions of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system 10. FIG. 2 depicts a mobile ad hoc network (“MANET”) 20, which may form a secure, temporal network connection 22 (sometimes connected and sometimes isolated), with a cloud networking and computing environment 30 or other remote networking system, so that network functions may occur over the MANET 20 within the environment, without the need for external networks, but at other times information can be sent to and from a central location. This allows the industrial environment to use the benefits of networking and control technologies, while also providing security, such as preventing cyber-attacks. The MANET 20 may use cognitive radio technologies 40, including those that form up an equivalent to the IP protocol, such as router 42, MAC 44, and physical layer technologies 46. In certain embodiments, the system depicted in FIGS. 1 through 5 provides network-sensitive or network-aware transport of data over the network to and from a data collection device or a heavy industrial machine.

[0236] FIGS. 3-4 depict intelligent data collection technologies deployed locally, at the edge of an IoT deployment, where heavy industrial machines are located. This includes various sensors 52, IoT devices 54, data storage capabilities (e.g., data pools 60, or distributed ledger system 62) (including intelligent, self-organizing storage), sensor fusion (including self-organizing sensor fusion), and the like. Interfaces for data collection, including multi-sensory interfaces, tablets, smartphones 58, and the like are shown. FIG. 3 also shows data pools 60 that may collect data published by machines or sensors that detect conditions of machines, such as for later consumption by local or remote intelligence. A distributed ledger system 62 may distribute storage across the local storage of various elements of the environment, or more broadly throughout the system. FIG. 4 also shows on-device sensor fusion 80, such as for storing on a device data from multiple analog sensors 82, which may be analyzed locally or in the cloud, such as by machine learning 84, including by training a machine based on initial models created by humans that are augmented by providing feedback (such as based on measures of success) when operating the methods and systems disclosed herein.

[0237] FIG. 1 depicts a server based portion of an industrial IoT system that may be deployed in the cloud or on an enterprise owner's or operator's premises. The server portion includes network coding (including self-organizing network coding and/or automated configuration) that may configure a network coding model based on feedback measures, network conditions, or the like, for highly efficient transport of large amounts of data across the network to and from data collection systems and the cloud. Network coding may provide a wide range of capabilities for intelligence, analytics, remote control, remote operation, remote optimization, various storage configurations and the like, as depicted in FIG. 1. The various storage configurations may include distributed ledger storage for supporting transactional data or other elements of the system.

[0238] FIG. 5 depicts a programmatic data marketplace 70, which may be a self-organizing marketplace, such as for making available data that is collected in industrial environments, such as from data collectors, data pools, distributed ledgers, and other elements disclosed herein. Additional detail on the various components and sub-components of FIGS. 1 through 5 is provided throughout this disclosure.

[0239] With reference to FIG. 6, an embodiment of platform 100 may include a local data collection system 102, which may be disposed in an environment 104, such as an industrial environment similar to that shown in FIG. 3, for collecting data from or about the elements of the environment, such as machines, components, systems, sub-systems, ambient conditions, states, workflows, processes, and other elements. The platform 100 may connect to or include portions of the industrial IoT data collection, monitoring and control system 10 depicted in FIGS. 1-5. The platform 100 may include a network data transport system 108, such as for transporting data to and from the local data collection system 102 over a network 110, such as to a host processing system 112, such as one that is disposed in a cloud computing environment or on the premises of an enterprise, or that consists of distributed components that interact with each other to process data collected by the local data collection system 102. The host processing system 112, referred to for convenience in some cases as the host system 112, may include various systems, components, methods, processes, facilities, and the like for enabling automated, or automation-assisted processing of the data, such as for monitoring one or more environments 104 or networks 110 or for remotely controlling one or more elements in a local environment 104 or in a network 110. The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated action based on the applications of a set of rules or models upon input data from the local data collection system 102 or from one or more input sources 116, which may comprise information feeds and inputs from a wide array of sources, including those in the local environment 104, in a network 110, in the host system 112, or in one or more external systems, databases, or the like. The platform 100 may include one or more intelligent systems 118, which may be disposed in, integrated with, or acting as inputs to one or more components of the platform 100. Details of these and other components of the platform 100 are provided throughout this disclosure.

[0240] Intelligent systems 118 may include cognitive systems 120, such as enabling a degree of cognitive behavior as a result of the coordination of processing elements, such as mesh, peer-to-peer, ring, serial, and other architectures, where one or more node elements is coordinated with other node elements to provide collective, coordinated behavior to assist in processing, communication, data collection, or the like. The MANET 20 depicted in FIG. 2 may also use cognitive radio technologies, including those that form up an equivalent to the IP protocol, such as router 42, MAC 44, and physical layer technologies 46. In one example, the cognitive system technology stack can include examples disclosed in U.S. Pat. No. 8,060,017 to Schlicht et al., issued 15 Nov. 2011 and hereby incorporated by reference as if fully set forth herein.

[0241] Intelligent systems may include machine learning systems 122, such as for learning on one or more data sets. The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like that may, in turn, be used for processing by the host system 112 as inputs to components of the platform 100 and portions of the industrial IoT data collection, monitoring and control system 10, or the like. Learning may be human-supervised or fully-automated, such as using one or more input sources 116 to provide a data set, along with information about the item to be learned. Machine learning may use one or more models, rules, semantic understandings, workflows, or other structured or semi-structured understanding of the world, such as for automated optimization of control of a system or process based on feedback or feed forward to an operating model for the system or process. One such machine learning technique for semantic and contextual understandings, workflows, or other structured or semi-structured understandings is disclosed in U.S. Pat. No. 8,200,775 to Moore, issued 12 Jun. 2012, and hereby incorporated by reference as if fully set forth herein. Machine learning may be used to improve the foregoing, such as by adjusting one or more weights, structures, rules, or the like (such as changing a function within a model) based on feedback (such as regarding the success of a model in a given situation) or based on iteration (such as in a recursive process). Where sufficient understanding of the underlying structure or behavior of a system is not known, insufficient data is not available, or in other cases where preferred for various reasons, machine learning may also be undertaken in the absence of an underlying model; that is, input sources may be weighted, structured, or the like within a machine learning facility without regard to any a priori understanding of structure, and outcomes (such as those based on measures of success at accomplishing various desired objectives) can be serially fed to the machine learning system to allow it to learn how to achieve the targeted objectives. For example, the system may learn to recognize faults, to recognize patterns, to develop models or functions, to develop rules, to optimize performance, to minimize failure rates, to optimize profits, to optimize resource utilization, to optimize flow (such as flow of traffic), or to optimize many other parameters that may be relevant to successful outcomes (such as outcomes in a wide range of environments). Machine learning may use genetic programming techniques, such as promoting or demoting one or more input sources, structures, data types, objects, weights, nodes, links, or other factors based on feedback (such that successful elements emerge over a series of generations). For example, alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like. In embodiments, local machine learning may turn on or off one or more sensors in a multi-sensor data collection system 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), contributing to optimization of one or more parameters, identification of a pattern (such as relating to a threat, a failure mode, a success mode, or the like) or the like. For example, a system may learn what sets of sensors should be turned on or off under given conditions to achieve the highest value utilization of a data collector 102. In embodiments, similar techniques may be used to handle optimization of transport of data in the platform 100 (such as in the network 110) by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring network security elements), and the like.

[0242] In embodiments, the local data collection system 102 may include a high-performance, multi-sensor data collector having a number of novel features for collection and processing of analog and other sensor data. In embodiments, a local data collection system 102 may be deployed to the industrial facilities depicted in FIG. 3. A local data collection system 102 may also be deployed monitor other machines such as the machine 2300 in FIG. 10 and FIG. 11, the machines 2400, 2600, 2800, 2950, 3000 depicted in FIG. 12, and the machines 3202, 3204 depicted in FIG. 13. The data collection system 102 may have on-board intelligent systems 118 (such as for learning to optimize the configuration and operation of the data collector, such as configuring permutations and combinations of sensors based on contexts and conditions). In one example, the data collection system 102 includes a crosspoint switch 130 or other analog switch. Automated, intelligent configuration of the local data collection system 102 may be based on a variety of types of information, such as information from various input sources, including those based on available power, power requirements of sensors, the value of the data collected (such as based on feedback information from other elements of the platform 100), the relative value of information (such as values based on the availability of other sources of the same or similar information), power availability (such as for powering sensors), network conditions, ambient conditions, operating states, operating contexts, operating events, and many others.

[0243] FIG. 7 shows elements and sub-components of a data collection and analysis system 1100 for sensor data (such as analog sensor data) collected in industrial environments. As depicted in FIG. 7, embodiments of the methods and systems disclosed herein may include hardware that has several different modules starting with the multiplexer (“MUX”) main board 1104. In embodiments, there may be a MUX option board 1108. The MUX 1104 main board is where the sensors connect to the system. These connections are on top to enable ease of installation. Then there are numerous settings on the underside of this board as well as on the Mux option board 1108, which attaches to the MUX main board 1104 via two headers one at either end of the board. In embodiments, the Mux option board has the male headers, which mesh together with the female header on the main Mux board. This enables them to be stacked on top of each other taking up less real estate.

[0244] In embodiments, the main Mux board and/or the MUX option board then connects to the mother (e.g., with 4 simultaneous channels) and daughter (e.g., with 4 additional channels for 8 total channels) analog boards 1110 via cables where some of the signal conditioning (such as hardware integration) occurs. The signals then move from the analog boards 1110 to an anti-aliasing board (not shown) where some of the potential aliasing is removed. The rest of the aliasing removal is done on the delta sigma board 1112. The delta sigma board 1112 provides more aliasing protection along with other conditioning and digitizing of the signal. Next, the data moves to the Jennic™ board 1114 for more digitizing as well as communication to a computer via USB or Ethernet. In embodiments, the Jennic™ board 1114 may be replaced with a pic board 1118 for more advanced and efficient data collection as well as communication. Once the data moves to the computer software 1102, the computer software 1102 can manipulate the data to show trending, spectra, waveform, statistics, and analytics.

[0245] In embodiments, the system is meant to take in all types of data from volts to 4-20 mA signals. In embodiments, open formats of data storage and communication may be used. In some instances, certain portions of the system may be proprietary especially some of research and data associated with the analytics and reporting. In embodiments, smart band analysis is a way to break data down into easily analyzed parts that can be combined with other smart bands to make new more simplified yet sophisticated analytics. In embodiments, this unique information is taken and graphics are used to depict the conditions because picture depictions are more helpful to the user. In embodiments, complicated programs and user interfaces are simplified so that any user can manipulate the data like an expert.

[0246] In embodiments, the system in essence, works in a big loop. The system starts in software with a general user interface (“GUI”) 1124. In embodiments, rapid route creation may take advantage of hierarchical templates. In embodiments, a GUI is created so any general user can populate the information itself with simple templates. Once the templates are created the user can copy and paste whatever the user needs. In addition, users can develop their own templates for future ease of use and to institutionalize the knowledge. When the user has entered all of the user's information and connected all of the user's sensors, the user can then start the system acquiring data.

[0247] Embodiments of the methods and systems disclosed herein may include unique electrostatic protection for trigger and vibration inputs. In many critical industrial environments where large electrostatic forces, which can harm electrical equipment, may build up, for example rotating machinery or low-speed balancing using large belts, proper transducer and trigger input protection is required. In embodiments, a low-cost but efficient method is described for such protection without the need for external supplemental devices.

[0248] Typically, vibration data collectors are not designed to handle large input voltages due to the expense and the fact that, more often than not, it is not needed. A need exists for these data collectors to acquire many varied types of RPM data as technology improves and monitoring costs plummet. In embodiments, a method is using the already established OptoMOS™ technology which permits the switching up front of high voltage signals rather than using more conventional reed-relay approaches. Many historic concerns regarding non-linear zero crossing or other non-linear solid-state behaviors have been eliminated with regard to the passing through of weakly buffered analog signals. In addition, in embodiments, printed circuit board routing topologies place all of the individual channel input circuitry as close to the input connector as possible. In embodiments, a unique electrostatic protection for trigger and vibration inputs may be placed upfront on the Mux and DAQ hardware in order to dissipate the built up electric charge as the signal passed from the sensor to the hardware. In embodiments, the Mux and analog board may support high-amperage input using a design topology comprising wider traces and solid state relays for upfront circuitry.

[0249] In some systems multiplexers are afterthoughts and the quality of the signal coming from the multiplexer is not considered. As a result of a poor quality multiplexer, the quality of the signal can drop as much as 30 dB or more. Thus, substantial signal quality may be lost using a 24-bit DAQ that has a signal to noise ratio of 110 dB and if the signal to noise ratio drops to 80 dB in the Mux, it may not be much better than a 16-bit system from 20 years ago. In embodiments of this system, an important part at the front of the Mux is upfront signal conditioning on Mux for improved signal-to-noise ratio. Embodiments may perform signal conditioning (such as range/gain control, integration, filtering, etc.) on vibration as well as other signal inputs up front before Mux switching to achieve the highest signal-to-noise ratio.

[0250] In embodiments, in addition to providing a better signal, the multiplexer may provide a continuous monitor alarming feature. Truly continuous systems monitor every sensor all the time but tend to be expensive. Typical multiplexer systems only monitor a set number of channels at one time and switch from bank to bank of a larger set of sensors. As a result, the sensors not being currently collected are not being monitored; if a level increases the user may never know. In embodiments, a multiplexer may have a continuous monitor alarming feature by placing circuitry on the multiplexer that can measure input channel levels against known alarm conditions even when the data acquisition (“DAQ”) is not monitoring the input. In embodiments, continuous monitoring Mux bypass offers a mechanism whereby channels not being currently sampled by the Mux system may be continuously monitored for significant alarm conditions via a number of trigger conditions using filtered peak-hold circuits or functionally similar that are in turn passed on to the monitoring system in an expedient manner using hardware interrupts or other means. This, in essence, makes the system continuously monitoring, although without the ability to instantly capture data on the problem like a true continuous system. In embodiments, coupling this capability to alarm with adaptive scheduling techniques for continuous monitoring and the continuous monitoring system's software adapting and adjusting the data collection sequence based on statistics, analytics, data alarms and dynamic analysis may allow the system to quickly collect dynamic spectral data on the alarming sensor very soon after the alarm sounds.

[0251] Another restriction of typical multiplexers is that they may have a limited number of channels. In embodiments, use of distributed complex programmable logic device (“CPLD”) chips with dedicated bus for logic control of multiple Mux and data acquisition sections enables a CPLD to control multiple mux and DAQs so that there is no limit to the number of channels a system can handle. Interfacing to multiple types of predictive maintenance and vibration transducers requires a great deal of switching. This includes AC/DC coupling, 4-20 interfacing, integrated electronic piezoelectric transducer, channel power-down (for conserving op-amp power), single-ended or differential grounding options, and so on. Also required is the control of digital pots for range and gain control, switches for hardware integration, AA filtering and triggering. This logic can be performed by a series of CPLD chips strategically located for the tasks they control. A single giant CPLD requires long circuit routes with a great deal of density at the single giant CPLD. In embodiments, distributed CPLDs not only address these concerns but offer a great deal of flexibility. A bus is created where each CPLD that has a fixed assignment has its own unique device address. In embodiments, multiplexers and DAQs can stack together offering additional input and output channels to the system. For multiple boards (e.g., for multiple Mux boards), jumpers are provided for setting multiple addresses. In another example, three bits permit up to 8 boards that are jumper configurable. In embodiments, a bus protocol is defined such that each CPLD on the bus can either be addressed individually or as a group.

[0252] Typical multiplexers may be limited to collecting only sensors in the same bank. For detailed analysis, this may be limiting as there is tremendous value in being able to simultaneously review data from sensors on the same machine. Current systems using conventional fixed bank multiplexers can only compare a limited number of channels (based on the number of channels per bank) that were assigned to a particular group at the time of installation. The only way to provide some flexibility is to either overlap channels or incorporate lots of redundancy in the system both of which can add considerable expense (in some cases an exponential increase in cost versus flexibility). The simplest Mux design selects one of many inputs and routes it into a single output line. A banked design would consist of a group of these simple building blocks, each handling a fixed group of inputs and routing to its respective output. Typically, the inputs are not overlapping so that the input of one Mux grouping cannot be routed into another. Unlike conventional Mux chips which typically switch a fixed group or banks of a fixed selection of channels into a single output (e.g., in groups of 2, 4, 8, etc.), a cross point Mux allows the user to assign any input to any output. Previously, crosspoint multiplexers were used for specialized purposes such as RGB digital video applications and were as a practical matter too noisy for analog applications such as vibration analysis; however more recent advances in the technology now make it feasible. Another advantage of the crosspoint Mux is the ability to disable outputs by putting them into a high impedance state. This is ideal for an output bus so that multiple Mux cards may be stacked, and their output buses joined together without the need for bus switches.

[0253] In embodiments, this may be addressed by use of an analog crosspoint switch for collecting variable groups of vibration input channels and providing a matrix circuit so the system may access any set of eight channels from the total number of input sensors.

[0254] In embodiments, the ability to control multiple multiplexers with use of distributed CPLD chips with dedicated bus for logic control of multiple Mux and data acquisition sections is enhanced with a hierarchical multiplexer which allows for multiple DAQ to collect data from multiple multiplexers. A hierarchical Mux may allow modularly output of more channels, such as 16, 24 or more to multiple of eight channel card sets. In embodiments, this allows for faster data collection as well as more channels of simultaneous data collection for more complex analysis. In embodiments, the Mux may be configured slightly to make it portable and use data acquisition parking features, which turns SV3X DAQ into a protected system embodiment.

[0255] In embodiments, once the signals leave the multiplexer and hierarchical Mux they move to the analog board where there are other enhancements. In embodiments, power saving techniques may be used such as: power-down of analog channels when not in use; powering down of component boards; power-down of analog signal processing op-amps for non-selected channels; powering down channels on the mother and the daughter analog boards. The ability to power down component boards and other hardware by the low-level firmware for the DAQ system makes high-level application control with respect to power-saving capabilities relatively easy. Explicit control of the hardware is always possible but not required by default. In embodiments, this power saving benefit may be of value to a protected system, especially if it is battery operated or solar powered.

[0256] In embodiments, in order to maximize the signal to noise ratio and provide the best data, a peak-detector for auto-scaling routed into a separate A/D will provide the system the highest peak in each set of data so it can rapidly scale the data to that peak. For vibration analysis purposes, the built-in A/D convertors in many microprocessors may be inadequate with regards to number of bits, number of channels or sampling frequency versus not slowing the microprocessor down significantly. Despite these limitations, it is useful to use them for purposes of auto-scaling. In embodiments, a separate A/D may be used that has reduced functionality and is cheaper. For each channel of input, after the signal is buffered (usually with the appropriate coupling: AC or DC) but before it is signal conditioned, the signal is fed directly into the microprocessor or low-cost A/D. Unlike the conditioned signal for which range, gain and filter switches are thrown, no switches are varied. This permits the simultaneous sampling of the auto-scaling data while the input data is signal conditioned, fed into a more robust external A/D, and directed into on-board memory using direct memory access (DMA) methods where memory is accessed without requiring a CPU. This significantly simplifies the auto-scaling process by not having to throw switches and then allow for settling time, which greatly slows down the auto-scaling process. Furthermore, the data may be collected simultaneously, which assures the best signal-to-noise ratio. The reduced number of bits and other features is usually more than adequate for auto-scaling purposes. In embodiments, improved integration using both analog and digital methods create an innovative hybrid integration which also improves or maintains the highest possible signal to noise ratio.

[0257] In embodiments, a section of the analog board may allow routing of a trigger channel, either raw or buffered, into other analog channels. This may allow a user to route the trigger to any of the channels for analysis and trouble shooting. Systems may have trigger channels for the purposes of determining relative phase between various input data sets or for acquiring significant data without the needless repetition of unwanted input. In embodiments, digitally controlled relays may be used to switch either the raw or buffered trigger signal into one of the input channels. It may be desirable to examine the quality of the triggering pulse because it may be corrupted for a variety of reasons including inadequate placement of the trigger sensor, wiring issues, faulty setup issues such as a dirty piece of reflective tape if using an optical sensor, and so on. The ability to look at either the raw or buffered signal may offer an excellent diagnostic or debugging vehicle. It also can offer some improved phase analysis capability by making use of the recorded data signal for various signal processing techniques such as variable speed filtering algorithms.

[0258] In embodiments, once the signals leave the analog board, the signals move into the delta-sigma board where precise voltage reference for A/D zero reference offers more accurate direct current sensor data. The delta sigma's high speeds also provide for using higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize antialiasing filter requirements. Lower oversampling rates can be used for higher sampling rates. For example, a 3^rd order AA filter set for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) is then adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoff AA filter can then be used for Fmax ranges from 1 kHz and higher (with a secondary filter kicking in at 2.56× the highest sampling rate of 128 kHz). In embodiments, a CPLD may be used as a clock-divider for a delta-sigma A/D to achieve lower sampling rates without the need for digital resampling. In embodiments, a high-frequency crystal reference can be divided down to lower frequencies by employing a CPLD as a programmable clock divider. The accuracy of the divided down lower frequencies is even more accurate than the original source relative to their longer time periods. This also minimizes or removes the need for resampling processing by the delta-sigma A/D.

[0259] In embodiments, the data then moves from the delta-sigma board to the Jennic™ board where phase relative to input and trigger channels using on-board timers may be digitally derived. In embodiments, the Jennic™ board also has the ability to store calibration data and system maintenance repair history data in an on-board card set. In embodiments, the Jennic™ board will enable acquiring long blocks of data at high-sampling rate as opposed to multiple sets of data taken at different sampling rates so it can stream data and acquire long blocks of data for advanced analysis in the future.

[0260] In embodiments, after the signal moves through the Jennic™ board it may then be transmitted to the computer. In embodiments, the computer software will be used to add intelligence to the system starting with an expert system GUI. The GUI will offer a graphical expert system with simplified user interface for defining smart bands and diagnoses which facilitate anyone to develop complex analytics. In embodiments, this user interface may revolve around smart bands, which are a simplified approach to complex yet flexible analytics for the general user. In embodiments, the smart bands may pair with a self-learning neural network for an even more advanced analytical approach. In embodiments, this system may use the machine's hierarchy for additional analytical insight. One critical part of predictive maintenance is the ability to learn from known information during repairs or inspections. In embodiments, graphical approaches for back calculations may improve the smart bands and correlations based on a known fault or problem.

[0261] In embodiments, there is a smart route which adapts which sensors it collects simultaneously in order to gain additional correlative intelligence. In embodiments, smart operational data store (“ODS”) allows the system to elect to gather data to perform operational deflection shape analysis in order to further examine the machinery condition. In embodiments, adaptive scheduling techniques allow the system to change the scheduled data collected for full spectral analysis across a number (e.g., eight), of correlative channels. In embodiments, the system may provide data to enable extended statistics capabilities for continuous monitoring as well as ambient local vibration for analysis that combines ambient temperature and local temperature and vibration levels changes for identifying machinery issues.

[0262] In embodiments, a data acquisition device may be controlled by a personal computer (PC) to implement the desired data acquisition commands. In embodiments, the DAQ box may be self-sufficient and can acquire, process, analyze and monitor independent of external PC control. Embodiments may include secure digital (SD) card storage. In embodiments, significant additional storage capability may be provided by utilizing an SD card. This may prove critical for monitoring applications where critical data may be stored permanently. Also, if a power failure should occur, the most recent data may be stored despite the fact that it was not off-loaded to another system.

[0263] A current trend has been to make DAQ systems as communicative as possible with the outside world usually in the form of networks including wireless. In the past it was common to use a dedicated bus to control a DAQ system with either a microprocessor or microcontroller/microprocessor paired with a PC. In embodiments, a DAQ system may comprise one or more microprocessor/microcontrollers, specialized microcontrollers/microprocessors, or dedicated processors focused primarily on the communication aspects with the outside world. These include USB, Ethernet and wireless with the ability to provide an IP address or addresses in order to host a webpage. All communications with the outside world are then accomplished using a simple text based menu. The usual array of commands (in practice more than a hundred) such as InitializeCard, AcquireData, StopAcquisition, RetrieveCalibration Info, and so on, would be provided.

[0264] In embodiments, intense signal processing activities including resampling, weighting, filtering, and spectrum processing may be performed by dedicated processors such as field-programmable gate array (“FPGAs”), digital signal processor (“DSP”), microprocessors, micro-controllers, or a combination thereof. In embodiments, this subsystem may communicate via a specialized hardware bus with the communication processing section. It will be facilitated with dual-port memory, semaphore logic, and so on. This embodiment will not only provide a marked improvement in efficiency but can significantly improve the processing capability, including the streaming of the data as well other high-end analytical techniques. This negates the need for constantly interrupting the main processes which include the control of the signal conditioning circuits, triggering, raw data acquisition using the A/D, directing the A/D output to the appropriate on-board memory and processing that data.

[0265] Embodiments may include sensor overload identification. A need exists for monitoring systems to identify when the sensor is overloading. There may be situations involving high-frequency inputs that will saturate a standard 100 mv/g sensor (which is most commonly used in the industry) and having the ability to sense the overload improves data quality for better analysis. A monitoring system may identify when their system is overloading, but in embodiments, the system may look at the voltage of the sensor to determine if the overload is from the sensor, enabling the user to get another sensor better suited to the situation, or gather the data again.

[0266] Embodiments may include radio frequency identification (“RFID”) and an inclinometer or accelerometer on a sensor so the sensor can indicate what machine/bearing it is attached to and what direction such that the software can automatically store the data without the user input. In embodiments, users could put the system on any machine or machines and the system would automatically set itself up and be ready for data collection in seconds.

[0267] Embodiments may include ultrasonic online monitoring by placing ultrasonic sensors inside transformers, motor control centers, breakers and the like and monitoring, via a sound spectrum, continuously looking for patterns that identify arcing, corona and other electrical issues indicating a break down or issue. Embodiments may include providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility. In embodiments, an analysis engine may be used in ultrasonic online monitoring as well as identifying other faults by combining the ultrasonic data with other parameters such as vibration, temperature, pressure, heat flux, magnetic fields, electrical fields, currents, voltage, capacitance, inductance, and combinations (e.g., simple ratios) of the same, among many others.

[0268] Embodiments of the methods and systems disclosed herein may include use of an analog crosspoint switch for collecting variable groups of vibration input channels. For vibration analysis, it is useful to obtain multiple channels simultaneously from vibration transducers mounted on different parts of a machine (or machines) in multiple directions. By obtaining the readings at the same time, for example, the relative phases of the inputs may be compared for the purpose of diagnosing various mechanical faults. Other types of cross channel analyses such as cross-correlation, transfer functions, Operating Deflection Shape (“ODS”) may also be performed.

[0269] Embodiments of the methods and systems disclosed herein may include precise voltage reference for A/D zero reference. Some A/D chips provide their own internal zero voltage reference to be used as a mid-scale value for external signal conditioning circuitry to ensure that both the A/D and external op-amps use the same reference. Although this sounds reasonable in principle, there are practical complications. In many cases these references are inherently based on a supply voltage using a resistor-divider. For many current systems, especially those whose power is derived from a PC via USB or similar bus, this provides for an unreliable reference, as the supply voltage will often vary quite significantly with load. This is especially true for delta-sigma A/D chips which necessitate increased signal processing. Although the offsets may drift together with load, a problem arises if one wants to calibrate the readings digitally. It is typical to modify the voltage offset expressed as counts coming from the A/D digitally to compensate for the DC drift. However, for this case, if the proper calibration offset is determined for one set of loading conditions, they will not apply for other conditions. An absolute DC offset expressed in counts will no longer be applicable. As a result, it becomes necessary to calibrate for all loading conditions which becomes complex, unreliable, and ultimately unmanageable. In embodiments, an external voltage reference is used which is simply independent of the supply voltage to use as the zero offset.

[0270] In embodiments, the system provides a phase-lock-loop band pass tracking filter method for obtaining slow-speed RPMs and phase for balancing purposes to remotely balance slow speed machinery, such as in paper mills, as well as offering additional analysis from its data. For balancing purposes, it is sometimes necessary to balance at very slow speeds. A typical tracking filter may be constructed based on a phase-lock loop or PLL design; however, stability and speed range are overriding concerns. In embodiments, a number of digitally controlled switches are used for selecting the appropriate RC and damping constants. The switching can be done all automatically after measuring the frequency of the incoming tachometer signal. Embodiments of the methods and systems disclosed herein may include digital derivation of phase relative to input and trigger channels using on-board timers. In embodiments, digital phase derivation uses digital timers to ascertain an exact delay from a trigger event to the precise start of data acquisition. This delay, or offset, then, is further refined using interpolation methods to obtain an even more precise offset which is then applied to the analytically determined phase of the acquired data such that the phase is “in essence” an absolute phase with precise mechanical meaning useful for among other things, one-shot balancing, alignment analysis, and so on.

[0271] Embodiments of the methods and systems disclosed herein may include signal processing firmware/hardware. In embodiments, long blocks of data may be acquired at high-sampling rate as opposed to multiple sets of data taken at different sampling rates. Typically, in modern route collection for vibration analysis, it is customary to collect data at a fixed sampling rate with a specified data length. The sampling rate and data length may vary from route point to point based on the specific mechanical analysis requirements at hand. For example, a motor may require a relatively low sampling rate with high resolution to distinguish running speed harmonics from line frequency harmonics. The practical trade-off here though is that it takes more collection time to achieve this improved resolution. In contrast, some high-speed compressors or gear sets require much higher sampling rates to measure the amplitudes of relatively higher frequency data although the precise resolution may not be as necessary. Ideally, however, it would be better to collect a very long sample length of data at a very high-sampling rate. When digital acquisition devices were first popularized in the early 1980's, the A/D sampling, digital storage, and computational abilities were not close to what they are today, so compromises were made between the time required for data collection and the desired resolution and accuracy. It was because of this limitation that some analysts in the field even refused to give up their analog tape recording systems, which did not suffer as much from these same digitizing drawbacks. A few hybrid systems were employed that would digitize the play back of the recorded analog data at multiple sampling rates and lengths desired, though these systems were admittedly less automated. The more common approach, as mentioned earlier, is to balance data collection time with analysis capability and digitally acquire the data blocks at multiple sampling rates and sampling lengths and digitally store these blocks separately. In embodiments, a long data length of data can be collected at the highest practical sampling rate (e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This long block of data can be acquired in the same amount of time as the shorter length of the lower sampling rates utilized by a priori methods so that there is no effective delay added to the sampling at the measurement point, always a concern in route collection. In embodiments, analog tape recording of data is digitally simulated with such a precision that it can be in effect considered continuous or “analog” for many purposes, including for purposes of embodiments of the present disclosure, except where context indicates otherwise.

[0272] Embodiments of the methods and systems disclosed herein may include storage of calibration data and maintenance history on-board card sets. Many data acquisition devices which rely on interfacing to a PC to function store their calibration coefficients on the PC. This is especially true for complex data acquisition devices whose signal paths are many and therefore whose calibration tables can be quite large. In embodiments, calibration coefficients are stored in flash memory which will remember this data or any other significant information for that matter, for all practical purposes, permanently. This information may include nameplate information such as serial numbers of individual components, firmware or software version numbers, maintenance history, and the calibration tables. In embodiments, no matter which computer the box is ultimately connected to, the DAQ box remains calibrated and continues to hold all of this critical information. The PC or external device may poll for this information at any time for implantation or information exchange purposes.

[0273] Embodiments of the methods and systems disclosed herein may include rapid route creation taking advantage of hierarchical templates. In the field of vibration monitoring, as well as parametric monitoring in general, it is necessary to establish in a database or functional equivalent the existence of data monitoring points. These points are associated a variety of attributes including the following categories: transducer attributes, data collection settings, machinery parameters and operating parameters. The transducer attributes would include probe type, probe mounting type and probe mounting direction or axis orientation. Data collection attributes associated with the measurement would involve a sampling rate, data length, integrated electronic piezoelectric probe power and coupling requirements, hardware integration requirements, 4-20 or voltage interfacing, range and gain settings (if applicable), filter requirements, and so on. Machinery parametric requirements relative to the specific point would include such items as operating speed, bearing type, bearing parametric data which for a rolling element bearing includes the pitch diameter, number of balls, inner race, and outer-race diameters. For a tilting pad bearing, this would include the number of pads and so on. For measurement points on a piece of equipment such as a gearbox, needed parameters would include, for example, the number of gear teeth on each of the gears. For induction motors, it would include the number of rotor bars and poles; for compressors, the number of blades and/or vanes; for fans, the number of blades. For belt/pulley systems, the number of belts as well as the relevant belt-passing frequencies may be calculated from the dimensions of the pulleys and pulley center-to-center distance. For measurements near couplings, the coupling type and number of teeth in a geared coupling may be necessary, and so on. Operating parametric data would include operating load, which may be expressed in megawatts, flow (either air or fluid), percentage, horsepower, feet-per-minute, and so on. Operating temperatures both ambient and operational, pressures, humidity, and so on, may also be relevant. As can be seen, the setup information required for an individual measurement point can be quite large. It is also crucial to performing any legitimate analysis of the data. Machinery, equipment, and bearing specific information are essential for identifying fault frequencies as well as anticipating the various kinds of specific faults to be expected. The transducer attributes as well as data collection parameters are vital for properly interpreting the data along with providing limits for the type of analytical techniques suitable. The traditional means of entering this data has been manual and quite tedious, usually at the lowest hierarchical level (for example, at the bearing level with regards to machinery parameters), and at the transducer level for data collection setup information. It cannot be stressed enough, however, the importance of the hierarchical relationships necessary to organize data—both for analytical and interpretive purposes as well as the storage and movement of data. Here, we are focusing primarily on the storage and movement of data. By its nature, the aforementioned setup information is extremely redundant at the level of the lowest hierarchies; however, because of its strong hierarchical nature, it can be stored quite efficiently in that form. In embodiments, hierarchical nature can be utilized when copying data in the form of templates. As an example, hierarchical storage structure suitable for many purposes is defined from general to specific of company, plant or site, unit or process, machine, equipment, shaft element, bearing, and transducer. It is much easier to copy data associated with a particular machine, piece of equipment, shaft element or bearing than it is to copy only at the lowest transducer level. In embodiments, the system not only stores data in this hierarchical fashion, but robustly supports the rapid copying of data using these hierarchical templates. Similarity of elements at specific hierarchical levels lends itself to effective data storage in hierarchical format. For example, so many machines have common elements such as motors, gearboxes, compressors, belts, fans, and so on. More specifically, many motors can be easily classified as induction, DC, fixed or variable speed. Many gearboxes can be grouped into commonly occurring groupings such as input/output, input pinion/intermediate pinion/output pinion, 4-posters, and so on. Within a plant or company, there are many similar types of equipment purchased and standardized on for both cost and maintenance reasons. This results in an enormous overlapping of similar types of equipment and, as a result, offers a great opportunity for taking advantage of a hierarchical template approach.

[0274] Embodiments of the methods and systems disclosed herein may include smart bands. Smart bands refer to any processed signal characteristics derived from any dynamic input or group of inputs for the purposes of analyzing the data and achieving the correct diagnoses. Furthermore, smart bands may even include mini or relatively simple diagnoses for the purposes of achieving a more robust and complex one. Historically, in the field of mechanical vibration analysis, Alarm Bands have been used to define spectral frequency bands of interest for the purposes of analyzing and/or trending significant vibration patterns. The Alarm Band typically consists of a spectral (amplitude plotted against frequency) region defined between a low and high frequency border. The amplitude between these borders is summed in the same manner for which an overall amplitude is calculated. A Smart Band is more flexible in that it not only refers to a specific frequency band but can also refer to a group of spectral peaks such as the harmonics of a single peak, a true-peak level or crest factor derived from a time waveform, an overall derived from a vibration envelope spectrum or other specialized signal analysis technique or a logical combination (AND, OR, XOR, etc.) of these signal attributes. In addition, a myriad assortment of other parametric data, including system load, motor voltage and phase information, bearing temperature, flow rates, and the like, can likewise be used as the basis for forming additional smart bands. In embodiments, Smart Band symptoms may be used as building blocks for an expert system whose engine would utilize these inputs to derive diagnoses. Some of these mini-diagnoses may then in turn be used as Smart-Band symptoms (smart bands can include even diagnoses) for more generalized diagnoses.

[0275] Embodiments of the methods and systems disclosed herein may include a neural net expert system using smart bands. Typical vibration analysis engines are rule-based (i.e., they use a list of expert rules which, when met, trigger specific diagnoses). In contrast, a neural approach utilizes the weighted triggering of multiple input stimuli into smaller analytical engines or neurons which in turn feed a simplified weighted output to other neurons. The output of these neurons can be also classified as smart bands which in turn feed other neurons. This produces a more layered approach to expert diagnosing as opposed to the one-shot approach of a rule-based system. In embodiments, the expert system utilizes this neural approach using smart bands; however, it does not preclude rule-based diagnoses being reclassified as smart bands as further stimuli to be utilized by the expert system. From this point-of-view, it can be overviewed as a hybrid approach, although at the highest level it is essentially neural.

[0276] Embodiments of the methods and systems disclosed herein may include use of database hierarchy in analysis smart band symptoms and diagnoses may be assigned to various hierarchical database levels. For example, a smart band may be called “Looseness” at the bearing level, trigger “Looseness” at the equipment level, and trigger “Looseness” at the machine level. Another example would be having a smart band diagnosis called “Horizontal Plane Phase Flip” across a coupling and generate a smart band diagnosis of “Vertical Coupling Misalignment” at the machine level.

[0277] Embodiments of the methods and systems disclosed herein may include expert system GUIs. In embodiments, the system undertakes a graphical approach to defining smart bands and diagnoses for the expert system. The entry of symptoms, rules, or more generally smart bands for creating a particular machine diagnosis, may be tedious and time consuming. One means of making the process more expedient and efficient is to provide a graphical means by use of wiring. The proposed graphical interface consists of four major components: a symptom parts bin, diagnoses bin, tools bin, and graphical wiring area (“GWA”). In embodiments, a symptom parts bin includes various spectral, waveform, envelope and any type of signal processing characteristic or grouping of characteristics such as a spectral peak, spectral harmonic, waveform true-peak, waveform crest-factor, spectral alarm band, and so on. Each part may be assigned additional properties. For example, a spectral peak part may be assigned a frequency or order (multiple) of running speed. Some parts may be pre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×, 3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars×running speed, and so on.

[0278] In embodiments, the diagnoses bin includes various pre-defined as well as user-defined diagnoses such as misalignment, imbalance, looseness, bearing faults, and so on. Like parts, diagnoses may also be used as parts for the purposes of building more complex diagnoses. In embodiments, the tools bin includes logical operations such as AND, OR, XOR, etc. or other ways of combining the various parts listed above such as Find Max, Find Min, Interpolate, Average, other Statistical Operations, etc. In embodiments, a graphical wiring area includes parts from the parts bin or diagnoses from the diagnoses bin and may be combined using tools to create diagnoses. The various parts, tools and diagnoses will be represented with icons which are simply graphically wired together in the desired manner.

[0279] Embodiments of the methods and systems disclosed herein may include a graphical approach for back-calculation definition. In embodiments, the expert system also provides the opportunity for the system to learn. If one already knows that a unique set of stimuli or smart bands corresponds to a specific fault or diagnosis, then it is possible to back-calculate a set of coefficients that when applied to a future set of similar stimuli would arrive at the same diagnosis. In embodiments, if there are multiple sets of data, a best-fit approach may be used. Unlike the smart band GUI, this embodiment will self-generate a wiring diagram. In embodiments, the user may tailor the back-propagation approach settings and use a database browser to match specific sets of data with the desired diagnoses. In embodiments, the desired diagnoses may be created or custom tailored with a smart band GUI. In embodiments, after that, a user may press the GENERATE button and a dynamic wiring of the symptom-to-diagnosis may appear on the screen as it works through the algorithms to achieve the best fit. In embodiments, when complete, a variety of statistics are presented which detail how well the mapping process proceeded. In some cases, no mapping may be achieved if, for example, the input data was all zero or the wrong data (mistakenly assigned) and so on. Embodiments of the methods and systems disclosed herein may include bearing analysis methods. In embodiments, bearing analysis methods may be used in conjunction with a computer aided design (“CAD”), predictive deconvolution, minimum variance distortionless response (“MVDR”) and spectrum sum-of-harmonics.

[0280] In recent years, there has been a strong drive to save power which has resulted in an influx of variable frequency drives and variable speed machinery. In embodiments, a bearing analysis method is provided. In embodiments, torsional vibration detection and analysis is provided utilizing transitory signal analysis to provide an advanced torsional vibration analysis for a more comprehensive way to diagnose machinery where torsional forces are relevant (such as machinery with rotating components). Due primarily to the decrease in cost of motor speed control systems, as well as the increased cost and consciousness of energy-usage, it has become more economically justifiable to take advantage of the potentially vast energy savings of load control. Unfortunately, one frequently overlooked design aspect of this issue is that of vibration. When a machine is designed to run at only one speed, it is far easier to design the physical structure accordingly so as to avoid mechanical resonances both structural and torsional, each of which can dramatically shorten the mechanical health of a machine. This would include such structural characteristics as the types of materials to use, their weight, stiffening member requirements and placement, bearing types, bearing location, base support constraints, etc. Even with machines running at one speed, designing a structure so as to minimize vibration can prove a daunting task, potentially requiring computer modeling, finite-element analysis, and field testing. By throwing variable speeds into the mix, in many cases, it becomes impossible to design for all desirable speeds. The problem then becomes one of minimization, e.g., by speed avoidance. This is why many modern motor controllers are typically programmed to skip or quickly pass through specific speed ranges or bands. Embodiments may include identifying speed ranges in a vibration monitoring system. Non-torsional, structural resonances are typically fairly easy to detect using conventional vibration analysis techniques. However, this is not the case for torsion. One special area of current interest is the increased incidence of torsional resonance problems, apparently due to the increased torsional stresses of speed change as well as the operation of equipment at torsional resonance speeds. Unlike non-torsional structural resonances which generally manifest their effect with dramatically increased casing or external vibration, torsional resonances generally show no such effect. In the case of a shaft torsional resonance, the twisting motion induced by the resonance may only be discernible by looking for speed and/or phase changes. The current standard methodology for analyzing torsional vibration involves the use of specialized instrumentation. Methods and systems disclosed herein allow analysis of torsional vibration without such specialized instrumentation. This may consist of shutting the machine down and employing the use of strain gauges and/or other special fixturing such as speed encoder plates and/or gears. Friction wheels are another alternative, but they typically require manual implementation and a specialized analyst. In general, these techniques can be prohibitively expensive and/or inconvenient. An increasing prevalence of continuous vibration monitoring systems due to decreasing costs and increasing convenience (e.g., remote access) exists. In embodiments, there is an ability to discern torsional speed and/or phase variations with just the vibration signal. In embodiments, transient analysis techniques may be utilized to distinguish torsionally induced vibrations from mere speed changes due to process control. In embodiments, factors for discernment might focus on one or more of the following aspects: the rate of speed change due to variable speed motor control would be relatively slow, sustained and deliberate; torsional speed changes would tend to be short, impulsive and not sustained; torsional speed changes would tend to be oscillatory, most likely decaying exponentially, process speed changes would not; and smaller speed changes associated with torsion relative to the shaft's rotational speed which suggest that monitoring phase behavior would show the quick or transient speed bursts in contrast to the slow phase changes historically associated with ramping a machine's speed up or down (as typified with Bode or Nyquist plots).

[0281] Embodiments of the methods and systems disclosed herein may include improved integration using both analog and digital methods. When a signal is digitally integrated using software, essentially the spectral low-end frequency data has its amplitude multiplied by a function which quickly blows up as it approaches zero and creates what is known in the industry as a “ski-slope” effect. The amplitude of the ski-slope is essentially the noise floor of the instrument. The simple remedy for this is the traditional hardware integrator, which can perform at signal-to-noise ratios much greater than that of an already digitized signal. It can also limit the amplification factor to a reasonable level so that multiplication by very large numbers is essentially prohibited. However, at high frequencies where the frequency becomes large, the original amplitude which may be well above the noise floor is multiplied by a very small number (1/f) that plunges it well below the noise floor. The hardware integrator has a fixed noise floor that although low floor does not scale down with the now lower amplitude high-frequency data. In contrast, the same digital multiplication of a digitized high-frequency signal also scales down the noise floor proportionally. In embodiments, hardware integration may be used below the point of unity gain where (at a value usually determined by units and/or desired signal to noise ratio based on gain) and software integration may be used above the value of unity gain to produce an ideal result. In embodiments, this integration is performed in the frequency domain. In embodiments, the resulting hybrid data can then be transformed back into a waveform which should be far superior in signal-to-noise ratio when compared to either hardware integrated or software integrated data. In embodiments, the strengths of hardware integration are used in conjunction with those of digital software integration to achieve the maximum signal-to-noise ratio. In embodiments, the first order gradual hardware integrator high pass filter along with curve fitting allow some relatively low frequency data to get through while reducing or eliminating the noise, allowing very useful analytical data that steep filters kill to be salvaged.

[0282] Embodiments of the methods and systems disclosed herein may include adaptive scheduling techniques for continuous monitoring. Continuous monitoring is often performed with an up-front Mux whose purpose it is to select a few channels of data among many to feed the hardware signal processing, A/D, and processing components of a DAQ system. This is done primarily out of practical cost considerations. The tradeoff is that all of the points are not monitored continuously (although they may be monitored to a lesser extent via alternative hardware methods). In embodiments, multiple scheduling levels are provided. In embodiments, at the lowest level, which is continuous for the most part, all of the measurement points will be cycled through in round-robin fashion. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 points, then each point is serviced once every 15 minutes; however, if a point should alarm by whatever criteria the user selects, its priority level can be increased so that it is serviced more often. As there can be multiple grades of severity for each alarm, so can there me multiple levels of priority with regards to monitoring. In embodiments, more severe alarms will be monitored more frequently. In embodiments, a number of additional high-level signal processing techniques can be applied at less frequent intervals. Embodiments may take advantage of the increased processing power of a PC and the PC can temporarily suspend the round-robin route collection (with its multiple tiers of collection) process and stream the required amount of data for a point of its choosing. Embodiments may include various advanced processing techniques such as envelope processing, wavelet analysis, as well as many other signal processing techniques. In embodiments, after acquisition of this data, the DAQ card set will continue with its route at the point it was interrupted. In embodiments, various PC scheduled data acquisitions will follow their own schedules which will be less frequency than the DAQ card route. They may be set up hourly, daily, by number of route cycles (for example, once every 10 cycles) and also increased scheduling-wise based on their alarm severity priority or type of measurement (e.g., motors may be monitored differently than fans).

[0283] Embodiments of the methods and systems disclosed herein may include data acquisition parking features. In embodiments, a data acquisition box used for route collection, real time analysis and in general as an acquisition instrument can be detached from its PC (tablet or otherwise) and powered by an external power supply or suitable battery. In embodiments, the data collector still retains continuous monitoring capability and its on-board firmware can implement dedicated monitoring functions for an extended period of time or can be controlled remotely for further analysis. Embodiments of the methods and systems disclosed herein may include extended statistical capabilities for continuous monitoring.

[0284] Embodiments of the methods and systems disclosed herein may include ambient sensing plus local sensing plus vibration for analysis. In embodiments, ambient environmental temperature and pressure, sensed temperature and pressure may be combined with long/medium term vibration analysis for prediction of any of a range of conditions or characteristics. Variants may add infrared sensing, infrared thermography, ultrasound, and many other types of sensors and input types in combination with vibration or with each other. Embodiments of the methods and systems disclosed herein may include a smart route. In embodiments, the continuous monitoring system's software will adapt/adjust the data collection sequence based on statistics, analytics, data alarms and dynamic analysis. Typically, the route is set based on the channels the sensors are attached to. In embodiments, with the crosspoint switch, the Mux can combine any input Mux channels to the (e.g., eight) output channels. In embodiments, as channels go into alarm or the system identifies key deviations, it will pause the normal route set in the software to gather specific simultaneous data, from the channels sharing key statistical changes, for more advanced analysis. Embodiments include conducting a smart ODS or smart transfer function.

[0285] Embodiments of the methods and systems disclosed herein may include smart ODS and one or more transfer functions. In embodiments, due to a system's multiplexer and crosspoint switch, an ODS, a transfer function, or other special tests on all the vibration sensors attached to a machine/structure can be performed and show exactly how the machine's points are moving in relationship to each other. In embodiments, 40-50 kHz and longer data lengths (e.g., at least one minute) may be streamed, which may reveal different information than what a normal OD S or transfer function will show. In embodiments, the system will be able to determine, based on the data/statistics/analytics to use, the smart route feature that breaks from the standard route and conducts an ODS across a machine, structure or multiple machines and structures that might show a correlation because the conditions/data directs it. In embodiments, for the transfer functions there may be an impact hammer used on one channel and then compared against other vibration sensors on the machine. In embodiments, the system may use the condition changes such as load, speed, temperature or other changes in the machine or system to conduct the transfer function. In embodiments, different transfer functions may be compared to each other over time. In embodiments, difference transfer functions may be strung together like a movie that may show how the machinery fault changes, such as a bearing that could show how it moves through the four stages of bearing failure and so on. Embodiments of the methods and systems disclosed herein may include a hierarchical Mux.

[0286] With reference to FIG. 8, the present disclosure generally includes digitally collecting or streaming waveform data 2010 from a machine 2020 whose operational speed can vary from relatively slow rotational or oscillational speeds to much higher speeds in different situations. The waveform data 2010, at least on one machine, may include data from a single axis sensor 2030 mounted at an unchanging reference location 2040 and from a three-axis sensor 2050 mounted at changing locations (or located at multiple locations), including location 2052. In embodiments, the waveform data 2010 can be vibration data obtained simultaneously from each sensor 2030, 2050 in a gap-free format for a duration of multiple minutes with maximum resolvable frequencies sufficiently large to capture periodic and transient impact events. By way of this example, the waveform data 2010 can include vibration data that can be used to create an operational deflecting shape. It can also be used, as needed, to diagnose vibrations from which a machine repair solution can be prescribed.

[0287] In embodiments, the machine 2020 can further include a housing 2100 that can contain a drive motor 2110 that can drive a shaft 2120. The shaft 2120 can be supported for rotation or oscillation by a set of bearings 2130, such as including a first bearing 2140 and a second bearing 2150. A data collection module 2160 can connect to (or be resident on) the machine 2020. In one example, the data collection module 2160 can be located and accessible through a cloud network facility 2170, can collect the waveform data 2010 from the machine 2020, and deliver the waveform data 2010 to a remote location. A working end 2180 of the drive shaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, a drill, a gear system, a drive system, or other working element, as the techniques described herein can apply to a wide range of machines, equipment, tools, or the like that include rotating or oscillating elements. In other instances, a generator can be substituted for the motor 2110, and the working end of the drive shaft 2120 can direct rotational energy to the generator to generate power, rather than consume it.

[0288] In embodiments, the waveform data 2010 can be obtained using a predetermined route format based on the layout of the machine 2020. The waveform data 2010 may include data from the single axis sensor 2030 and the three-axis sensor 2050. The single-axis sensor 2030 can serve as a reference probe with its one channel of data and can be fixed at the unchanging location 2040 on the machine under survey. The three-axis sensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes) with its three channels of data and can be moved along a predetermined diagnostic route format from one test point to the next test point. In one example, both sensors 2030, 2050 can be mounted manually to the machine 2020 and can connect to a separate portable computer in certain service examples. The reference probe can remain at one location while the user can move the tri-axial vibration probe along the predetermined route, such as from bearing-to-bearing on a machine. In this example, the user is instructed to locate the sensors at the predetermined locations to complete the survey (or portion thereof) of the machine.

[0289] With reference to FIG. 9, a portion of an exemplary machine 2200 is shown having a tri-axial sensor 2210 mounted to a location 2220 associated with a motor bearing of the machine 2200 with an output shaft 2230 and output member 2240 in accordance with the present disclosure. With reference to FIG. 10, an exemplary machine 2300 is shown having a tri-axial sensor 2310 and a single-axis vibration sensor 2320 serving as the reference sensor that is attached on the machine 2300 at an unchanging location for the duration of the vibration survey in accordance with the present disclosure. The tri-axial sensor 2310 and the single-axis vibration sensor 2320 can be connected to a data collection system 2330.

[0290] In further examples, the sensors and data acquisition modules and equipment can be integral to, or resident on, the rotating machine. By way of these examples, the machine can contain many single axis sensors and many tri-axial sensors at predetermined locations. The sensors can be originally installed equipment and provided by the original equipment manufacturer or installed at a different time in a retrofit application. The data collection module 2160, or the like, can select and use one single axis sensor and obtain data from it exclusively during the collection of waveform data 2010 while moving to each of the tri-axial sensors. The data collection module 2160 can be resident on the machine 2020 and/or connect via the cloud network facility 2170.

[0291] With reference to FIG. 8, the various embodiments include collecting the waveform data 2010 by digitally recording locally, or streaming over, the cloud network facility 2170. The waveform data 2010 can be collected so as to be gap-free with no interruptions and, in some respects, can be similar to an analog recording of waveform data. The waveform data 2010 from all of the channels can be collected for one to two minutes depending on the rotating or oscillating speed of the machine being monitored. In embodiments, the data sampling rate can be at a relatively high-sampling rate relative to the operating frequency of the machine 2020.

[0292] In embodiments, a second reference sensor can be used, and a fifth channel of data can be collected. As such, the single-axis sensor can be the first channel and tri-axial vibration can occupy the second, the third, and the fourth data channels. This second reference sensor, like the first, can be a single axis sensor, such as an accelerometer. In embodiments, the second reference sensor, like the first reference sensor, can remain in the same location on the machine for the entire vibration survey on that machine. The location of the first reference sensor (i.e., the single axis sensor) may be different than the location of the second reference sensors (i.e., another single axis sensor). In certain examples, the second reference sensor can be used when the machine has two shafts with different operating speeds, with the two reference sensors being located on the two different shafts. In accordance with this example, further single-axis reference sensors can be employed at additional but different unchanging locations associated with the rotating machine.

[0293] In embodiments, the waveform data can be transmitted electronically in a gap-free free format at a significantly high rate of sampling for a relatively longer period of time. In one example, the period of time is 60 seconds to 120 seconds. In another example, the rate of sampling is 100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will be appreciated in light of this disclosure that the waveform data can be shown to approximate more closely some of the wealth of data available from previous instances of analog recording of waveform data.

[0294] In embodiments, sampling, band selection, and filtering techniques can permit one or more portions of a long stream of data (i.e., one to two minutes in duration) to be under sampled or over sampled to realize varying effective sampling rates. To this end, interpolation and decimation can be used to further realize varying effective sampling rates. For example, oversampling may be applied to frequency bands that are proximal to rotational or oscillational operating speeds of the sampled machine, or to harmonics thereof, as vibration effects may tend to be more pronounced at those frequencies across the operating range of the machine. In embodiments, the digitally-sampled data set can be decimated to produce a lower sampling rate. It will be appreciated in light of the disclosure that decimate in this context can be the opposite of interpolate. In embodiments, decimating the data set can include first applying a low-pass filter to the digitally-sampled data set and then undersampling the data set.

[0295] In one example, a sample waveform at 100 Hz can be undersampled at every tenth point of the digital waveform to produce an effective sampling rate of 10 Hz, but the remaining nine points of that portion of the waveform are effectively discarded and not included in the modeling of the sample waveform. Moreover, this type of bare undersampling can create ghost frequencies due to the undersampling rate (i.e., 10 Hz) relative to the 100 Hz sample waveform.

[0296] Most hardware for analog-to-digital conversions uses a sample-and-hold circuit that can charge up a capacitor for a given amount of time such that an average value of the waveform is determined over a specific change in time. It will be appreciated in light of the disclosure that the value of the waveform over the specific change in time is not linear but more similar to a cardinal sinusoidal (“sinc”) function; therefore, it can be shown that more emphasis can be placed on the waveform data at the center of the sampling interval with exponential decay of the cardinal sinusoidal signal occurring from its center.

[0297] By way of the above example, the sample waveform at 100 Hz can be hardware-sampled at 10 Hz and therefore each sampling point is averaged over 100 milliseconds (e.g., a signal sampled at 100 Hz can have each point averaged over 10 milliseconds). In contrast to the effective discarding of nine out of the ten data points of the sampled waveform as discussed above, the present disclosure can include weighing adjacent data. The adjacent data can refer to the sample points that were previously discarded and the one remaining point that was retained. In one example, a low pass filter can average the adjacent sample data linearly, i.e., determining the sum of every ten points and then dividing that sum by ten. In a further example, the adjacent data can be weighted with a sinc function. The process of weighting the original waveform with the sinc function can be referred to as an impulse function, or can be referred to in the time domain as a convolution.

[0298] The present disclosure can be applicable to not only digitizing a waveform signal based on a detected voltage, but can also be applicable to digitizing waveform signals based on current waveforms, vibration waveforms, and image processing signals including video signal rasterization. In one example, the resizing of a window on a computer screen can be decimated, albeit in at least two directions. In these further examples, it will be appreciated that undersampling by itself can be shown to be insufficient. To that end, oversampling or upsampling by itself can similarly be shown to be insufficient, such that interpolation can be used like decimation but in lieu of only undersampling by itself.

[0299] It will be appreciated in light of the disclosure that interpolation in this context can refer to first applying a low pass filter to the digitally-sampled waveform data and then upsampling the waveform data. It will be appreciated in light of the disclosure that real-world examples can often require the use of use non-integer factors for decimation or interpolation, or both. To that end, the present disclosure includes interpolating and decimating sequentially in order to realize a non-integer factor rate for interpolating and decimating. In one example, interpolating and decimating sequentially can define applying a low-pass filter to the sample waveform, then interpolating the waveform after the low-pass filter, and then decimating the waveform after the interpolation. In embodiments, the vibration data can be looped to purposely emulate conventional tape recorder loops, with digital filtering techniques used with the effective splice to facilitate longer analyses. It will be appreciated in light of the disclosure that the above techniques do not preclude waveform, spectrum, and other types of analyses to be processed and displayed with a GUI of the user at the time of collection. It will be appreciated in light of the disclosure that newer systems can permit this functionality to be performed in parallel to the high-performance collection of the raw waveform data.

[0300] With respect to time of collection issues, it will be appreciated that older systems using the compromised approach of improving data resolution, by collecting at different sampling rates and data lengths, do not in fact save as much time as expected. To that end, every time the data acquisition hardware is stopped and started, latency issues can be created, especially when there is hardware auto-scaling performed. The same can be true with respect to data retrieval of the route information (i.e., test locations) that is often in a database format and can be exceedingly slow. The storage of the raw data in bursts to disk (whether solid state or otherwise) can also be undesirably slow.

[0301] In contrast, the many embodiments include digitally streaming the waveform data 2010, as disclosed herein, and also enjoying the benefit of needing to load the route parameter information while setting the data acquisition hardware only once. Because the waveform data 2010 is streamed to only one file, there is no need to open and close files, or switch between loading and writing operations with the storage medium. It can be shown that the collection and storage of the waveform data 2010, as described herein, can be shown to produce relatively more meaningful data in significantly less time than the traditional batch data acquisition approach. An example of this includes an electric motor about which waveform data can be collected with a data length of 4K points (i.e., 4,096) for sufficiently high resolution in order to, among other things, distinguish electrical sideband frequencies. For fans or blowers, a reduced resolution of 1K (i.e., 1,024) can be used. In certain instances, 1K can be the minimum waveform data length requirement. The sampling rate can be 1,280 Hz and that equates to an Fmax of 500 Hz. It will be appreciated in light of the disclosure that oversampling by an industry standard factor of 2.56 can satisfy the necessary two-times (2×) oversampling for the Nyquist Criterion with some additional leeway that can accommodate anti-aliasing filter-rolloff. The time to acquire this waveform data would be 1,024 points at 1,280 hertz, which are 800 milliseconds.

[0302] To improve accuracy, the waveform data can be averaged. Eight averages can be used with, for example, fifty percent overlap. This would extend the time from 800 milliseconds to 3.6 seconds, which is equal to 800 msec×8 averages×0.5 (overlap ratio)+0.5×800 msec (non-overlapped head and tail ends). After collection at Fmax=500 Hz waveform data, a higher sampling rate can be used. In one example, ten times (10×) the previous sampling rate can be used and Fmax=10 kHz. By way of this example, eight averages can be used with fifty percent (50%) overlap to collect waveform data at this higher rate that can amount to a collection time of 360 msec or 0.36 seconds. It will be appreciated in light of the disclosure that it can be necessary to read the hardware collection parameters for the higher sampling rate from the route list, as well as permit hardware auto-scaling, or the resetting of other necessary hardware collection parameters, or both. To that end, a few seconds of latency can be added to accommodate the changes in sampling rate. In other instances, introducing latency can accommodate hardware autoscaling and changes to hardware collection parameters that can be required when using the lower sampling rate disclosed herein. In addition to accommodating the change in sampling rate, additional time is needed for reading the route point information from the database (i.e., where to monitor and where to monitor next), displaying the route information, and processing the waveform data. Moreover, display of the waveform data and/or associated spectra can also consume significant time. In light of the above, 15 seconds to 20 seconds can elapse while obtaining waveform data at each measurement point.

[0303] In further examples, additional sampling rates can be added but this can make the total amount time for the vibration survey even longer because time adds up from changeover time from one sampling rate to another and from the time to obtain additional data at different sampling rate. In one example, a lower sampling rate is used, such as a sampling rate of 128 Hz where Fmax=50 Hz. By way of this example, the vibration survey would, therefore, require an additional 36 seconds for the first set of averaged data at this sampling rate, in addition to others mentioned above, and consequently the total time spent at each measurement point increases even more dramatically. Further embodiments include using similar digital streaming of gap free waveform data as disclosed herein for use with wind turbines and other machines that can have relatively slow speed rotating or oscillating systems. In many examples, the waveform data collected can include long samples of data at a relatively high-sampling rate. In one example, the sampling rate can be 100 kHz and the sampling duration can be for two minutes on all of the channels being recorded. In many examples, one channel can be for the single axis reference sensor and three more data channels can be for the tri-axial three channel sensor. It will be appreciated in light of the disclosure that the long data length can be shown to facilitate detection of extremely low frequency phenomena. The long data length can also be shown to accommodate the inherent speed variability in wind turbine operations. Additionally, the long data length can further be shown to provide the opportunity for using numerous averages such as those discussed herein, to achieve very high spectral resolution, and to make feasible tape loops for certain spectral analyses. Many multiple advanced analytical techniques can now become available because such techniques can use the available long uninterrupted length of waveform data in accordance with the present disclosure.

[0304] It will also be appreciated in light of the disclosure that the simultaneous collection of waveform data from multiple channels can facilitate performing transfer functions between multiple channels. Moreover, the simultaneous collection of waveform data from multiple channels facilitates establishing phase relationships across the machine so that more sophisticated correlations can be utilized by relying on the fact that the waveforms from each of the channels are collected simultaneously. In other examples, more channels in the data collection can be used to reduce the time it takes to complete the overall vibration survey by allowing for simultaneous acquisition of waveform data from multiple sensors that otherwise would have to be acquired, in a subsequent fashion, moving sensor to sensor in the vibration survey.

[0305] The present disclosure includes the use of at least one of the single-axis reference probe on one of the channels to allow for acquisition of relative phase comparisons between channels. The reference probe can be an accelerometer or other type of transducer that is not moved and, therefore, fixed at an unchanging location during the vibration survey of one machine. Multiple reference probes can each be deployed as at suitable locations fixed in place (i.e., at unchanging locations) throughout the acquisition of vibration data during the vibration survey. In certain examples, up to seven reference probes can be deployed depending on the capacity of the data collection module 2160 or the like. Using transfer functions or similar techniques, the relative phases of all channels may be compared with one another at all selected frequencies. By keeping the one or more reference probes fixed at their unchanging locations while moving or monitoring the other tri-axial vibration sensors, it can be shown that the entire machine can be mapped with regard to amplitude and relative phase. This can be shown to be true even when there are more measurement points than channels of data collection. With this information, an operating deflection shape can be created that can show dynamic movements of the machine in 3 D, which can provide an invaluable diagnostic tool. In embodiments, the one or more reference probes can provide relative phase, rather than absolute phase. It will be appreciated in light of the disclosure that relative phase may not be as valuable absolute phase for some purposes, but the relative phase the information can still be shown to be very useful.

[0306] In embodiments, the sampling rates used during the vibration survey can be digitally synchronized to predetermined operational frequencies that can relate to pertinent parameters of the machine such as rotating or oscillating speed. Doing this, permits extracting even more information using synchronized averaging techniques. It will be appreciated in light of the disclosure that this can be done without the use of a key phasor or a reference pulse from a rotating shaft, which is usually not available for route collected data. As such, nonsynchronous signals can be removed from a complex signal without the need to deploy synchronous averaging using the key phasor. This can be shown to be very powerful when analyzing a particular pinion in a gearbox or generally applied to any component within a complicated mechanical mechanism. In many instances, the key phasor or the reference pulse is rarely available with route collected data, but the techniques disclosed herein can overcome this absence. In embodiments, there can be multiple shafts running at different speeds within the machine being analyzed. In certain instances, there can be a single-axis reference probe for each shaft. In other instances, it is possible to relate the phase of one shaft to another shaft using only one single axis reference probe on one shaft at its unchanging location. In embodiments, variable speed equipment can be more readily analyzed with relatively longer duration of data relative to single speed equipment. The vibration survey can be conducted at several machine speeds within the same contiguous set of vibration data using the same techniques disclosed herein. These techniques can also permit the study of the change of the relationship between vibration and the change of the rate of speed that was not available before.

[0307] In embodiments, there are numerous analytical techniques that can emerge from because raw waveform data can be captured in a gap-free digital format as disclosed herein. The gap-free digital format can facilitate many paths to analyze the waveform data in many ways after the fact to identify specific problems. The vibration data collected in accordance with the techniques disclosed herein can provide the analysis of transient, semi-periodic and very low frequency phenomena. The waveform data acquired in accordance with the present disclosure can contain relatively longer streams of raw gap-free waveform data that can be conveniently played back as needed, and on which many and varied sophisticated analytical techniques can be performed. A large number of such techniques can provide for various forms of filtering to extract low amplitude modulations from transient impact data that can be included in the relatively longer stream of raw gap-free waveform data. It will be appreciated in light of the disclosure that in past data collection practices, these types of phenomena were typically lost by the averaging process of the spectral processing algorithms because the goal of the previous data acquisition module was purely periodic signals; or these phenomena were lost to file size reduction methodologies due to the fact that much of the content from an original raw signal was typically discarded knowing it would not be used.

[0308] In embodiments, there is a method of monitoring vibration of a machine having at least one shaft supported by a set of bearings. The method includes monitoring a first data channel assigned to a single-axis sensor at an unchanging location associated with the machine. The method also includes monitoring a second, third, and fourth data channel assigned to a three-axis sensor. The method further includes recording gap-free digital waveform data simultaneously from all of the data channels while the machine is in operation; and determining a change in relative phase based on the digital waveform data. The method also includes the tri-axial sensor being located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors on all of their channels simultaneously.

[0309] The method also includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location of the reference sensor is a position associated with a shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with a shaft of the machine and, wherein, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine. The various embodiments include methods of sequentially monitoring vibration or similar process parameters and signals of a rotating or oscillating machine or analogous process machinery from a number of channels simultaneously, which can be known as an ensemble. In various examples, the ensemble can include one to eight channels. In further examples, an ensemble can represent a logical measurement grouping on the equipment being monitored whether those measurement locations are temporary for measurement, supplied by the original equipment manufacturer, retrofit at a later date, or one or more combinations thereof.

[0310] In one example, an ensemble can monitor bearing vibration in a single direction. In a further example, an ensemble can monitor three different directions (e.g., orthogonal directions) using a tri-axial sensor. In yet further examples, an ensemble can monitor four or more channels where the first channel can monitor a single axis vibration sensor, and the second, the third, and the fourth channels can monitor each of the three directions of the tri-axial sensor. In other examples, the ensemble can be fixed to a group of adjacent bearings on the same piece of equipment or an associated shaft. The various embodiments provide methods that include strategies for collecting waveform data from various ensembles deployed in vibration studies or the like in a relatively more efficient manner. The methods also include simultaneously monitoring of a reference channel assigned to an unchanging reference location associated with the ensemble monitoring the machine. The cooperation with the reference channel can be shown to support a more complete correlation of the collected waveforms from the ensembles. The reference sensor on the reference channel can be a single axis vibration sensor, or a phase reference sensor that can be triggered by a reference location on a rotating shaft or the like. As disclosed herein, the methods can further include recording gap-free digital waveform data simultaneously from all of the channels of each ensemble at a relatively high rate of sampling so as to include all frequencies deemed necessary for the proper analysis of the machinery being monitored while it is in operation. The data from the ensembles can be streamed gap-free to a storage medium for subsequent processing that can be connected to a cloud network facility, a local data link, Bluetooth™ connectivity, cellular data connectivity, or the like.

[0311] In embodiments, the methods disclosed herein include strategies for collecting data from the various ensembles including digital signal processing techniques that can be subsequently applied to data from the ensembles to emphasize or better isolate specific frequencies or waveform phenomena. This can be in contrast with current methods that collect multiple sets of data at different sampling rates, or with different hardware filtering configurations including integration, that provide relatively less post-processing flexibility because of the commitment to these same (known as a priori hardware configurations). These same hardware configurations can also be shown to increase time of the vibration survey due to the latency delays associated with configuring the hardware for each independent test. In embodiments, the methods for collecting data from various ensembles include data marker technology that can be used for classifying sections of streamed data as homogenous and belonging to a specific ensemble. In one example, a classification can be defined as operating speed. In doing so, a multitude of ensembles can be created from what conventional systems would collect as only one. The many embodiments include post-processing analytic techniques for comparing the relative phases of all the frequencies of interest not only between each channel of the collected ensemble but also between all of the channels of all of the ensembles being monitored, when applicable.

[0312] With reference to FIG. 12, the many embodiments include a first machine 2400 having rotating or oscillating components 2410, or both, each supported by a set of bearings 2420 including a bearing pack 2422, a bearing pack 2424, a bearing pack 2426, and more as needed. The first machine 2400 can be monitored by a first sensor ensemble 2450. The first ensemble 2450 can be configured to receive signals from sensors originally installed (or added later) on the first machine 2400. The sensors on the machine 2400 can include single-axis sensors 2460, such as a single-axis sensor 2462, a single-axis sensor 2464, and more as needed. In many examples, the single-axis sensors 2460 can be positioned in the machine 2400 at locations that allow for the sensing of one of the rotating or oscillating components 2410 of the machine 2400.

[0313] The machine 2400 can also have tri-axial (e.g., orthogonal axes) sensors 2480, such as a tri-axial sensor 2482, a tri-axial sensor 2484, and more as needed. In many examples, the tri-axial sensors 2480 can be positioned in the machine 2400 at locations that allow for the sensing of one of each of the bearing packs in the sets of bearings 2420 that is associated with the rotating or oscillating components of the machine 2400. The machine 2400 can also have temperature sensors 2500, such as a temperature sensor 2502, a temperature sensor 2504, and more as needed. The machine 2400 can also have a tachometer sensor 2510 or more as needed that each detail the RPMs of one of its rotating components. By way of the above example, the first sensor ensemble 2450 can survey the above sensors associated with the first machine 2400. To that end, the first ensemble 2450 can be configured to receive eight channels. In other examples, the first sensor ensemble 2450 can be configured to have more than eight channels, or less than eight channels as needed. In this example, the eight channels include two channels that can each monitor a single-axis reference sensor signal and three channels that can monitor a tri-axial sensor signal. The remaining three channels can monitor two temperature signals and a signal from a tachometer. In one example, the first ensemble 2450 can monitor the single-axis sensor 2462, the single-axis sensor 2464, the tri-axial sensor 2482, the temperature sensor 2502, the temperature sensor 2504, and the tachometer sensor 2510 in accordance with the present disclosure. During a vibration survey on the machine 2400, the first ensemble 2450 can first monitor the tri-axial sensor 2482 and then move next to the tri-axial sensor 2484.

[0314] After monitoring the tri-axial sensor 2484, the first ensemble 2450 can monitor additional tri-axial sensors on the machine 2400 as needed and that are part of the predetermined route list associated with the vibration survey of the machine 2400, in accordance with the present disclosure. During this vibration survey, the first ensemble 2450 can continually monitor the single-axis sensor 2462, the single-axis sensor 2464, the two temperature sensors 2502, 2504, and the tachometer sensor 2510 while the first ensemble 2450 can serially monitor the multiple tri-axial sensors 2480 in the pre-determined route plan for this vibration survey.

[0315] With reference to FIG. 12, the many embodiments include a second machine 2600 having rotating or oscillating components 2610, or both, each supported by a set of bearings 2620 including a bearing pack 2622, a bearing pack 2624, a bearing pack 2626, and more as needed. The second machine 2600 can be monitored by a second sensor ensemble 2650. The second ensemble 2650 can be configured to receive signals from sensors originally installed (or added later) on the second machine 2600. The sensors on the machine 2600 can include single-axis sensors 2660, such as a single-axis sensor 2662, a single-axis sensor 2664, and more as needed. In many examples, the single-axis sensors 2660 can be positioned in the machine 2600 at locations that allow for the sensing of one of the rotating or oscillating components 2610 of the machine 2600.

[0316] The machine 2600 can also have tri-axial (e.g., orthogonal axes) sensors 2680, such as a tri-axial sensor 2682, a tri-axial sensor 2684, a tri-axial sensor 2686, a tri-axial sensor 2688, and more as needed. In many examples, the tri-axial sensors 2680 can be positioned in the machine 2600 at locations that allow for the sensing of one of each of the bearing packs in the sets of bearings 2620 that is associated with the rotating or oscillating components of the machine 2600. The machine 2600 can also have temperature sensors 2700, such as a temperature sensor 2702, a temperature sensor 2704, and more as needed. The machine 2600 can also have a tachometer sensor 2710 or more as needed that each detail the RPMs of one of its rotating components.

[0317] By way of the above example, the second sensor ensemble 2650 can survey the above sensors associated with the second machine 2600. To that end, the second ensemble 2650 can be configured to receive eight channels. In other examples, the second sensor ensemble 2650 can be configured to have more than eight channels or less than eight channels as needed. In this example, the eight channels include one channel that can monitor a single-axis reference sensor signal and six channels that can monitor two tri-axial sensor signals. The remaining channel can monitor a temperature signal. In one example, the second ensemble 2650 can monitor the single axis sensor 2662, the tri-axial sensor 2682, the tri-axial sensor 2684, and the temperature sensor 2702. During a vibration survey on the machine 2600 in accordance with the present disclosure, the second ensemble 2650 can first monitor the tri-axial sensor 2682 simultaneously with the tri-axial sensor 2684 and then move onto the tri-axial sensor 2686 simultaneously with the tri-axial sensor 2688.

[0318] After monitoring the tri-axial sensors 2680, the second ensemble 2650 can monitor additional tri-axial sensors (in simultaneous pairs) on the machine 2600 as needed and that are part of the predetermined route list associated with the vibration survey of the machine 2600 in accordance with the present disclosure. During this vibration survey, the second ensemble 2650 can continually monitor the single-axis sensor 2662 at its unchanging location and the temperature sensor 2702 while the second ensemble 2650 can serially monitor the multiple tri-axial sensors in the pre-determined route plan for this vibration survey.

[0319] With continuing reference to FIG. 12, the many embodiments include a third machine 2800 having rotating or oscillating components 2810, or both, each supported by a set of bearings 2820 including a bearing pack 2822, a bearing pack 2824, a bearing pack 2826, and more as needed. The third machine 2800 can be monitored by a third sensor ensemble 2850. The third ensemble 2850 can be configured with a single-axis sensor 2860, and two tri-axial (e.g., orthogonal axes) sensors 2880, 2882. In many examples, the single-axis sensor 2860 can be secured by the user on the machine 2800 at a location that allows for the sensing of one of the rotating or oscillating components of the machine 2800. The tri-axial sensors 2880, 2882 can be also be located on the machine 2800 by the user at locations that allow for the sensing of one of each of the bearings in the sets of bearings that each associated with the rotating or oscillating components of the machine 2800. The third ensemble 2850 can also include a temperature sensor 2900. The third ensemble 2850 and its sensors can be moved to other machines unlike the first and second ensembles 2450, 2650.

[0320] The many embodiments also include a fourth machine 2950 having rotating or oscillating components 2960, or both, each supported by a set of bearings 2970 including a bearing pack 2972, a bearing pack 2974, a bearing pack 2976, and more as needed. The fourth machine 2950 can be also monitored by the third sensor ensemble 2850 when the user moves it to the fourth machine 2950. The many embodiments also include a fifth machine 3000 having rotating or oscillating components 3010, or both. The fifth machine 3000 may not be explicitly monitored by any sensor or any sensor ensembles in operation but it can create vibrations or other impulse energy of sufficient magnitude to be recorded in the data associated with any one of the machines 2400, 2600, 2800, 2950 under a vibration survey.

[0321] The many embodiments include monitoring the first sensor ensemble 2450 on the first machine 2400 through the predetermined route as disclosed herein. The many embodiments also include monitoring the second sensor ensemble 2650 on the second machine 2600 through the predetermined route. The locations of machine 2400 being close to machine 2600 can be included in the contextual metadata of both vibration surveys. The third ensemble 2850 can be moved between machine 2800, machine 2950, and other suitable machines. The machine 3000 has no sensors onboard as configured, but could be monitored as needed by the third sensor ensemble 2850. The machine 3000 and its operational characteristics can be recorded in the metadata in relation to the vibration surveys on the other machines to note its contribution due to its proximity.

[0322] The many embodiments include hybrid database adaptation for harmonizing relational metadata and streaming raw data formats. Unlike older systems that utilized traditional database structure for associating nameplate and operational parameters (sometimes deemed metadata) with individual data measurements that are discrete and relatively simple, it will be appreciated in light of the disclosure that more modern systems can collect relatively larger quantities of raw streaming data with higher sampling rates and greater resolutions. At the same time, it will also be appreciated in light of the disclosure that the network of metadata with which to link and obtain this raw data or correlate with this raw data, or both, is expanding at ever-increasing rates.

[0323] In one example, a single overall vibration level can be collected as part of a route or prescribed list of measurement points. This data collected can then be associated with database measurement location information for a point located on a surface of a bearing housing on a specific piece of the machine adjacent to a coupling in a vertical direction. Machinery analysis parameters relevant to the proper analysis can be associated with the point located on the surface. Examples of machinery analysis parameters relevant to the proper analysis can include a running speed of a shaft passing through the measurement point on the surface. Further examples of machinery analysis parameters relevant to the proper analysis can include one of, or a combination of: running speeds of all component shafts for that piece of equipment and/or machine, bearing types being analyzed such as sleeve or rolling element bearings, the number of gear teeth on gears should there be a gearbox, the number of poles in a motor, slip and line frequency of a motor, roller bearing element dimensions, number of fan blades, or the like. Examples of machinery analysis parameters relevant to the proper analysis can further include machine operating conditions such as the load on the machines and whether load is expressed in percentage, wattage, air flow, head pressure, horsepower, and the like. Further examples of machinery analysis parameters include information relevant to adjacent machines that might influence the data obtained during the vibration study.

[0324] It will be appreciated in light of the disclosure that the vast array of equipment and machinery types can support many different classifications, each of which can be analyzed in distinctly different ways. For example, some machines, like screw compressors and hammer mills, can be shown to run much noisier and can be expected to vibrate significantly more than other machines. Machines known to vibrate more significantly can be shown to require a change in vibration levels that can be considered acceptable relative to quieter machines.

[0325] The present disclosure further includes hierarchical relationships found in the vibrational data collected that can be used to support proper analysis of the data. One example of the hierarchical data includes the interconnection of mechanical componentry such as a bearing being measured in a vibration survey and the relationship between that bearing, including how that bearing connects to a particular shaft on which is mounted a specific pinion within a particular gearbox, and the relationship between the shaft, the pinion, and the gearbox. The hierarchical data can further include in what particular spot within a machinery gear train that the bearing being monitored is located relative to other components in the machine. The hierarchical data can also detail whether the bearing being measured in a machine is in close proximity to another machine whose vibrations may affect what is being measured in the machine that is the subject of the vibration study.

[0326] The analysis of the vibration data from the bearing or other components related to one another in the hierarchical data can use table lookups, searches for correlations between frequency patterns derived from the raw data, and specific frequencies from the metadata of the machine. In some embodiments, the above can be stored in and retrieved from a relational database. In embodiments, National Instrument's Technical Data Management Solution (TDMS) file format can be used. The TDMS file format can be optimized for streaming various types of measurement data (i.e., binary digital samples of waveforms), as well as also being able to handle hierarchical metadata.

[0327] The many embodiments include a hybrid relational metadata-binary storage approach (HRM-BSA). The HRM-BSA can include a structured query language (SQL) based relational database engine. The structured query language based relational database engine can also include a raw data engine that can be optimized for throughput and storage density for data that is flat and relatively structureless. It will be appreciated in light of the disclosure that benefits can be shown in the cooperation between the hierarchical metadata and the SQL relational database engine. In one example, marker technologies and pointer sign-posts can be used to make correlations between the raw database engine and the SQL relational database engine. Three examples of correlations between the raw database engine and the SQL relational database engine linkages include: (1) pointers from the SQL database to the raw data; (2) pointers from the ancillary metadata tables or similar grouping of the raw data to the SQL database; and (3) independent storage tables outside the domain of either the SQL database or raw data technologies.

[0328] With reference to FIG. 13, the present disclosure can include pointers for Group 1 and Group 2 that can include associated filenames, path information, table names, database key fields as employed with existing SQL database technologies that can be used to associate a specific database segments or locations, asset properties to specific measurement raw data streams, records with associated time/date stamps, or associated metadata such as operating parameters, panel conditions, and the like. By way of this example, a plant 3200 can include machine one 3202, machine two 3204, and many others in the plant 3200. The machine one 3202 can include a gearbox 3212, a motor 3210, and other elements. The machine two 3204 can include a motor 3220, and other elements. Many waveforms 3230 including waveform one 3240, waveform two 3242, waveform three 3244, and additional waveforms as needed can be acquired from the machines 3202, 3204 in the plant 3200. The waveforms 3230 can be associated with the local marker linking tables 3300 and the linking raw data tables 3400. The machines 3202, 3204 and their elements can be associated with linking tables having relational databases 3500. The linking raw data tables 3400 and the linking tables having relational databases 3500 can be associated with the linking tables with optional independent storage tables 3600.

[0329] The present disclosure can include markers that can be applied to a time mark or a sample length within the raw waveform data. The markers generally fall into two categories: preset or dynamic. The preset markers can correlate to preset or existing operating conditions (e.g., load, head pressure, air flow cubic feet per minute, ambient temperature, RPMs, and the like). These preset markers can be fed into the data acquisition system directly. In certain instances, the preset markers can be collected on data channels in parallel with the waveform data (e.g., waveforms for vibration, current, voltage, etc.). Alternatively, the values for the preset markers can be entered manually.

[0330] For dynamic markers such as trending data, it can be important to compare similar data like comparing vibration amplitudes and patterns with a repeatable set of operating parameters. One example of the present disclosure includes one of the parallel channel inputs being a key phasor trigger pulse from an operating shaft that can provide RPM information at the instantaneous time of collection. In this example of dynamic markers, sections of collected waveform data can be marked with appropriate speeds or speed ranges.

[0331] The present disclosure can also include dynamic markers that can correlate to data that can be derived from post processing and analytics performed on the sample waveform. In further embodiments, the dynamic markers can also correlate to post-collection derived parameters including RPMs, as well as other operationally derived metrics such as alarm conditions like a maximum RPM. In certain examples, many modern pieces of equipment that are candidates for a vibration survey with the portable data collection systems described herein do not include tachometer information. This can be true because it is not always practical or cost-justifiable to add a tachometer even though the measurement of RPM can be of primary importance for the vibration survey and analysis. It will be appreciated that for fixed speed machinery obtaining an accurate RPM measurement can be less important especially when the approximate speed of the machine can be ascertained before-hand; however, variable-speed drives are becoming more and more prevalent. It will also be appreciated in light of the disclosure that various signal processing techniques can permit the derivation of RPM from the raw data without the need for a dedicated tachometer signal.

[0332] In many embodiments, the RPM information can be used to mark segments of the raw waveform data over its collection history. Further embodiments include techniques for collecting instrument data following a prescribed route of a vibration study. The dynamic markers can enable analysis and trending software to utilize multiple segments of the collection interval indicated by the markers (e.g., two minutes) as multiple historical collection ensembles, rather than just one as done in previous systems where route collection systems would historically store data for only one RPM setting. This could, in turn, be extended to any other operational parameter such as load setting, ambient temperature, and the like, as previously described. The dynamic markers, however, that can be placed in a type of index file pointing to the raw data stream can classify portions of the stream in homogenous entities that can be more readily compared to previously collected portions of the raw data stream

[0333] The many embodiments include the hybrid relational metadata-binary storage approach that can use the best of pre-existing technologies for both relational and raw data streams. In embodiments, the hybrid relational metadata-binary storage approach can marry them together with a variety of marker linkages. The marker linkages can permit rapid searches through the relational metadata and can allow for more efficient analyses of the raw data using conventional SQL techniques with pre-existing technology. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional database technologies do not provide.

[0334] The marker linkages can also permit rapid and efficient storage of the raw data using conventional binary storage and data compression techniques. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional raw data technologies provide such as TDMS (National Instruments), UFF (Universal File Format such as UFF58), and the like. The marker linkages can further permit using the marker technology links where a vastly richer set of data from the ensembles can be amassed in the same collection time as more conventional systems. The richer set of data from the ensembles can store data snapshots associated with predetermined collection criterion and the proposed system can derive multiple snapshots from the collected data streams utilizing the marker technology. In doing so, it can be shown that a relatively richer analysis of the collected data can be achieved. One such benefit can include more trending points of vibration at a specific frequency or order of running speed versus RPM, load, operating temperature, flow rates, and the like, which can be collected for a similar time relative to what is spent collecting data with a conventional system.

[0335] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines, elements of the machines and the environment of the machines including heavy duty machines deployed at a local job site or at distributed job sites under common control. The heavy-duty machines may include earthmoving equipment, heavy duty on-road industrial vehicles, heavy duty off-road industrial vehicles, industrial machines deployed in various settings such as turbines, turbomachinery, generators, pumps, pulley systems, manifold and valve systems, and the like. In embodiments, heavy industrial machinery may also include earth-moving equipment, earth-compacting equipment, hauling equipment, hoisting equipment, conveying equipment, aggregate production equipment, equipment used in concrete construction, and piledriving equipment. In examples, earth moving equipment may include excavators, backhoes, loaders, bulldozers, skid steer loaders, trenchers, motor graders, motor scrapers, crawler loaders, and wheeled loading shovels. In examples, construction vehicles may include dumpers, tankers, tippers, and trailers. In examples, material handling equipment may include cranes, conveyors, forklift, and hoists. In examples, construction equipment may include tunnel and handling equipment, road rollers, concrete mixers, hot mix plants, road making machines (compactors), stone crashers, pavers, slurry seal machines, spraying and plastering machines, and heavy-duty pumps. Further examples of heavy industrial equipment may include different systems such as implement traction, structure, power train, control, and information. Heavy industrial equipment may include many different powertrains and combinations thereof to provide power for locomotion and to also provide power to accessories and onboard functionality. In each of these examples, the platform 100 may deploy the local data collection system 102 into the environment 104 in which these machines, motors, pumps, and the like, operate and directly connected integrated into each of the machines, motors, pumps, and the like.

[0336] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines in operation and machines in being constructed such as turbine and generator sets like Siemens™ SGT6-5000F™ gas turbine, an SST-900™ steam turbine, an SGen6-1000A™ generator, and an SGen6-100A™ generator, and the like. In embodiments, the local data collection system 102 may be deployed to monitor steam turbines as they rotate in the currents caused by hot water vapor that may be directed through the turbine but otherwise generated from a different source such as from gas-fired burners, nuclear cores, molten salt loops and the like. In these systems, the local data collection system 102 may monitor the turbines and the water or other fluids in a closed loop cycle in which water condenses and is then heated until it evaporates again. The local data collection system 102 may monitor the steam turbines separately from the fuel source deployed to heat the water to steam. In examples, working temperatures of steam turbines may be between 50° and 650° C. In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressure, so they may optimally convert the respective steam pressure into rotational movement.

[0337] The local data collection system 102 may also be deployed in a gas turbines arrangement and therefore not only monitor the turbine in operation but also monitor the hot combustion gases feed into the turbine that may be in excess of 1,500° C. Because these gases are much hotter than those in steam turbines, the blades may be cooled with air that may flow out of small openings to create a protective film or boundary layer between the exhaust gases and the blades. This temperature profile may be monitored by the local data collection system 102. Gas turbine engines, unlike typical steam turbines, include a compressor, a combustion chamber, and a turbine all of which are journaled for rotation with a rotating shaft. The construction and operation of each of these components may be monitored by the local data collection system 102.

[0338] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from water turbines serving as rotary engines that may harvest energy from moving water and are used for electric power generation. The type of water turbine or hydro-power selected for a project may be based on the height of standing water, often referred to as head, and the flow (or volume of water) at the site. In this example, a generator may be placed at the top of a shaft that connects to the water turbine. As the turbine catches the naturally moving water in its blade and rotates, the turbine sends rotational power to the generator to generate electrical energy. In doing so, the platform 100 may monitor signals from the generators, the turbines, the local water system, flow controls such as dam windows and sluices. Moreover, the platform 100 may monitor local conditions on the electric grid including load, predicted demand, frequency response, and the like, and include such information in the monitoring and control deployed by the platform 100 in these hydroelectric settings.

[0339] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources. In embodiments, elements in such systems may include transmission lines, heat exchangers, desulphurization scrubbers, pumps, coolers, recuperators, chillers, and the like. In embodiments, certain implementations of turbomachinery, turbines, scroll compressors, and the like may be configured in arrayed control so as to monitor large facilities creating electricity for consumption, providing refrigeration, creating steam for local manufacture and heating, and the like, and that arrayed control platforms may be provided by the provider of the industrial equipment such as Honeywell and their Experion™ PKS platform. In embodiments, the platform 100 may specifically communicate with and integrate the local manufacturer-specific controls and may allow equipment from one manufacturer to communicate with other equipment. Moreover, the platform 100 provides allows for the local data collection system 102 to collect information across systems from many different manufacturers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from marine industrial equipment, marine diesel engines, shipbuilding, oil and gas plants, refineries, petrochemical plant, ballast water treatment solutions, marine pumps and turbines, and the like.

[0340] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from heavy industrial equipment and processes including monitoring one or more sensors. By way of this example, sensors may be devices that may be used to detect or respond to some type of input from a physical environment, such as an electrical, heat, or optical signal. In embodiments, the local data collection system 102 may include multiple sensors such as, without limitation, a temperature sensor, a pressure sensor, a torque sensor, a flow sensor, a heat sensor, a smoke sensor, an arc sensor, a radiation sensor, a position sensor, an acceleration sensor, a strain sensor, a pressure cycle sensor, a pressure sensor, an air temperature sensor, and the like. The torque sensor may encompass a magnetic twist angle sensor. In one example, the torque and speed sensors in the local data collection system 102 may be similar to those discussed in U.S. Pat. No. 8,352,149 to Meachem, issued 8 Jan. 2013 and hereby incorporated by reference as if fully set forth herein. In embodiments, one or more sensors may be provided such as a tactile sensor, a biosensor, a chemical sensor, an image sensor, a humidity sensor, an inertial sensor, and the like.

[0341] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors that may provide signals for fault detection including excessive vibration, incorrect material, incorrect material properties, trueness to the proper size, trueness to the proper shape, proper weight, trueness to balance. Additional fault sensors include those for inventory control and for inspections such as to confirm that parts are packaged to plan, parts are to tolerance in a plan, occurrence of packaging damage or stress, and sensors that may indicate the occurrence of shock or damage in transit. Additional fault sensors may include detection of the lack of lubrication, over lubrication, the need for cleaning of the sensor detection window, the need for maintenance due to low lubrication, the need for maintenance due to blocking or reduced flow in a lubrication region, and the like.

[0342] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 that includes aircraft operations and manufacture including monitoring signals from sensors for specialized applications such as sensors used in an aircraft's Attitude and Heading Reference System (AHRS), such as gyroscopes, accelerometers, and magnetometers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from image sensors such as semiconductor charge coupled devices (CCDs), active pixel sensors, in complementary metal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor (NMOS, Live MOS) technologies. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as an infra-red (IR) sensor, an ultraviolet (UV) sensor, a touch sensor, a proximity sensor, and the like. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors configured for optical character recognition (OCR), reading barcodes, detecting surface acoustic waves, detecting transponders, communicating with home automation systems, medical diagnostics, health monitoring, and the like.

[0343] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as a Micro-Electro-Mechanical Systems (MEMS) sensor, such as STMicroelectronics™ LSM303AH smart MEMS sensor, which may include an ultra-low-power high-performance system-in-package featuring a 3D digital linear acceleration sensor and a 3D digital magnetic sensor.

[0344] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from additional large machines such as turbines, windmills, industrial vehicles, robots, and the like. These large mechanical machines include multiple components and elements providing multiple subsystems on each machine. To that end, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from individual elements such as axles, bearings, belts, buckets, gears, shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums, dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals, sockets, sleeves, valves, wheels, actuators, motors, servomotor, and the like. Many of the machines and their elements may include servomotors. The local data collection system 102 may monitor the motor, the rotary encoder, and the potentiometer of the servomechanism to provide three-dimensional detail of position, placement, and progress of industrial processes.

[0345] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from gear drives, powertrains, transfer cases, multispeed axles, transmissions, direct drives, chain drives, belt-drives, shaft-drives, magnetic drives, and similar meshing mechanical drives. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from fault conditions of industrial machines that may include overheating, noise, grinding gears, locked gears, excessive vibration, wobbling, under-inflation, over-inflation, and the like. Operation faults, maintenance indicators, and interactions from other machines may cause maintenance or operational issues may occur during operation, during installation, and during maintenance. The faults may occur in the mechanisms of the industrial machines but may also occur in infrastructure that supports the machine such as its wiring and local installation platforms. In embodiments, the large industrial machines may face different types of fault conditions such as overheating, noise, grinding gears, excessive vibration of machine parts, fan vibration problems, problems with large industrial machines rotating parts.

[0346] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from industrial machinery including failures that may be caused by premature bearing failure that may occur due to contamination or loss of bearing lubricant. In another example, a mechanical defect such as misalignment of bearings may occur. Many factors may contribute to the failure such as metal fatigue, therefore, the local data collection system 102 may monitor cycles and local stresses. By way of this example, the platform 100 may monitor the incorrect operation of machine parts, lack of maintenance and servicing of parts, corrosion of vital machine parts, such as couplings or gearboxes, misalignment of machine parts, and the like. Though the fault occurrences cannot be completely stopped, many industrial breakdowns may be mitigated to reduce operational and financial losses. The platform 100 provides real-time monitoring and predictive maintenance in many industrial environments wherein it has been shown to present a cost-savings over regularly-scheduled maintenance processes that replace parts according to a rigid expiration of time and not actual load and wear and tear on the element or machine. To that end, the platform 10 may provide reminders of, or perform some, preventive measures such as adhering to operating manual and mode instructions for machines, proper lubrication, and maintenance of machine parts, minimizing or eliminating overrun of machines beyond their defined capacities, replacement of worn but still functional parts as needed, properly training the personnel for machine use, and the like.

[0347] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor multiple signals that may be carried by a plurality of physical, electronic, and symbolic formats or signals. The platform 100 may employ signal processing including a plurality of mathematical, statistical, computational, heuristic, and linguistic representations and processing of signals and a plurality of operations needed for extraction of useful information from signal processing operations such as techniques for representation, modeling, analysis, synthesis, sensing, acquisition, and extraction of information from signals. In examples, signal processing may be performed using a plurality of techniques, including but not limited to transformations, spectral estimations, statistical operations, probabilistic and stochastic operations, numerical theory analysis, data mining, and the like. The processing of various types of signals forms the basis of many electrical or computational process. As a result, signal processing applies to almost all disciplines and applications in the industrial environment such as audio and video processing, image processing, wireless communications, process control, industrial automation, financial systems, feature extraction, quality improvements such as noise reduction, image enhancement, and the like. Signal processing for images may include pattern recognition for manufacturing inspections, quality inspection, and automated operational inspection and maintenance. The platform 100 may employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations and may be used in applications such as computer vision, speech and text processing, radar processing, handwriting recognition, CAD systems, and the like. The platform 100 may employ supervised classification and unsupervised classification. The supervised learning classification algorithms may be based to create classifiers for image or pattern recognition, based on training data obtained from different object classes. The unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering. For example, some of the analysis techniques used in unsupervised learning may include K-means clustering, Gaussian mixture models, Hidden Markov models, and the like. The algorithms used in supervised and unsupervised learning methods of pattern recognition enable the use of pattern recognition in various high precision applications. The platform 100 may use pattern recognition in face detection related applications such as security systems, tracking, sports related applications, fingerprint analysis, medical and forensic applications, navigation and guidance systems, vehicle tracking, public infrastructure systems such as transport systems, license plate monitoring, and the like.

[0348] In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 using machine learning to enable derivation-based learning outcomes from computers without the need to program them. The platform 100 may, therefore, learn from and make decisions on a set of data, by making data-driven predictions and adapting according to the set of data. In embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may include presenting a set of example inputs and desired outputs to the machine learning systems. Unsupervised learning may include the learning algorithm itself structuring its input by methods such as pattern detection and/or feature learning. Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions. In examples, machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may also be classified as machine learning problems such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost and adaboost, K-nearest neighbors (KNN), a priori algorithms, and the like. In embodiments, certain machine learning algorithms may be used (such as genetic algorithms defined for solving both constrained and unconstrained optimization problems that may be based on natural selection, the process that drives biological evolution). By way of this example, genetic algorithms may be deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear. In an example, the genetic algorithm may be used to address problems of mixed integer programming, where some components restricted to being integer-valued. Genetic algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. By way of this example, the machine learning systems may be used to perform intelligent computing based control and be responsive to tasks in a wide variety of systems (such as interactive websites and portals, brain-machine interfaces, online security and fraud detection systems, medical applications such as diagnosis and therapy assistance systems, classification of DNA sequences, and the like). In examples, machine learning systems may be used in advanced computing applications (such as online advertising, natural language processing, robotics, search engines, software engineering, speech and handwriting recognition, pattern matching, game playing, computational anatomy, bioinformatics systems and the like). In an example, machine learning may also be used in financial and marketing systems (such as for user behavior analytics, online advertising, economic estimations, financial market analysis, and the like).

[0349] Additional details are provided below in connection with the methods, systems, devices, and components depicted in connection with FIGS. 1 through 6. In embodiments, methods and systems are disclosed herein for cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. For example, data streams from vibration, pressure, temperature, accelerometer, magnetic, electrical field, and other analog sensors may be multiplexed or otherwise fused, relayed over a network, and fed into a cloud-based machine learning facility, which may employ one or more models relating to an operating characteristic of an industrial machine, an industrial process, or a component or element thereof. A model may be created by a human who has experience with the industrial environment and may be associated with a training data set (such as models created by human analysis or machine analysis of data that is collected by the sensors in the environment, or sensors in other similar environments. The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs, such as classification of data into types, recognition of certain patterns (such as those indicating the presence of faults, orthoses indicating operating conditions, such as fuel efficiency, energy production, or the like). The machine learning facility may take feedback, such as one or more inputs or measures of success, such that it may train, or improve, its initial model (such as improvements by adjusting weights, rules, parameters, or the like, based on the feedback). For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and/or consumed e.g., such as through combustion, through chemical reactions in battery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other elements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like). For example, if a resonance effect between two machines is adversely affecting one of them, the model may account for this and automatically provide an output that results in changing the operation of one of the machines (such as to reduce the resonance, to increase fuel efficiency of one or both machines). By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like). This may be used to increase fuel efficiency, to reduce wear, to increase output, to increase operating life, to avoid fault conditions, and for many other purposes.

[0350] FIG. 14 illustrates components and interactions of a data collection architecture involving the application of cognitive and machine learning systems to data collection and processing. Referring to FIG. 14, a data collection system 102 may be disposed in an environment (such as an industrial environment where one or more complex systems, such as electro-mechanical systems and machines are manufactured, assembled, or operated). The data collection system 102 may include onboard sensors and may take input, such as through one or more input interfaces or ports 4008, from one or more sensors (such as analog or digital sensors of any type disclosed herein) and from one or more input sources 116 (such as sources that may be available through Wi-Fi, Bluetooth, NFC, or other local network connections or over the Internet). Sensors may be combined and multiplexed (such as with one or more multiplexers 4002). Data may be cached or buffered in a cache/buffer 4022 and made available to external systems, such as a remote host processing system 112 as described elsewhere in this disclosure (which may include an extensive processing architecture 4024, including any of the elements described in connection with other embodiments described throughout this disclosure and in the Figure), though one or more output interfaces and ports 4010 (which may in embodiments be separate from or the same as the input interfaces and ports 4008). The data collection system 102 may be configured to take input from a host processing system 112, such as input from an analytic system 4018, which may operate on data from the data collection system 102 and data from other input sources 116 to provide analytic results, which in turn may be provided as a learning feedback input 4012 to the data collection system, such as to assist in configuration and operation of the data collection system 102.

[0351] Combination of inputs (including selection of what sensors or input sources to turn “on” or “off”) may be performed under the control of machine-based intelligence, such as using a local cognitive input selection system 4004, an optionally remote cognitive input selection system 4014, or a combination of the two. The cognitive input selection systems 4004, 4014 may use intelligence and machine learning capabilities described elsewhere in this disclosure, such as using detected conditions (such as conditions informed by the input sources 116 or sensors), state information (including state information determined by a machine state recognition system 4020 that may determine a state), such as relating to an operational state, an environmental state, a state within a known process or workflow, a state involving a fault or diagnostic condition, or many others. This may include optimization of input selection and configuration based on learning feedback from the learning feedback system 4012, which may include providing training data (such as from the host processing system 112 or from other data collection systems 102 either directly or from the host 112) and may include providing feedback metrics, such as success metrics calculated within the analytic system 4018 of the host processing system 112. For example, if a data stream consisting of a particular combination of sensors and inputs yields positive results in a given set of conditions (such as providing improved pattern recognition, improved prediction, improved diagnosis, improved yield, improved return on investment, improved efficiency, or the like), then metrics relating to such results from the analytic system 4018 can be provided via the learning feedback system 4012 to the cognitive input selection systems 4004, 4014 to help configure future data collection to select that combination in those conditions (allowing other input sources to be de-selected, such as by powering down the other sensors). In embodiments, selection and de-selection of sensor combinations, under control of one or more of the cognitive input selection systems 4004, may occur with automated variation, such as using genetic programming techniques, based on learning feedback 4012, such as from the analytic system 4018, effective combinations for a given state or set of conditions are promoted, and less effective combinations are demoted, resulting in progressive optimization and adaptation of the local data collection system to each unique environment. Thus, an automatically adapting, multi-sensor data collection system is provided, where cognitive input selection is used (with feedback) to improve the effectiveness, efficiency, or other performance parameters of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results, energy production or usage, and the like), analytic metrics (such as success in recognizing patterns, making predictions, classifying data, or the like), and local system metrics (such as bandwidth utilization, storage utilization, power consumption, and the like). In embodiments, the analytic system 4018, the state system 4020 and the cognitive input selection system 4014 of a host may take data from multiple data collection systems 102, such that optimization (including of input selection) may be undertaken through coordinated operation of multiple data collection systems 102. For example, the cognitive input selection system 4014 may understand that if one data collection system 102 is already collecting vibration data for an X-axis, the X-axis vibration sensor for the other data collection system might be turned off, in favor of getting Y-axis data from the other data collection system 102. Thus, through coordinated collection by the host cognitive input selection system 4014, the activity of multiple data collection systems 102, across a host of different sensors, can provide for a rich data set for the host processing system 112, without wasting energy, bandwidth, storage space, or the like. As noted above, optimization may be based on overall system success metrics, analytic success metrics, and local system metrics, or a combination of the above.

[0352] Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. In embodiments, machine learning may take advantage of a state machine, such as tracking states of multiple analog and/or digital sensors, feeding the states into a pattern analysis facility, and determining anticipated states of the industrial system based on historical data about sequences of state information. For example, where a temperature state of an industrial machine exceeds a certain threshold and is followed by a fault condition, such as breaking down of a set of bearings, that temperature state may be tracked by a pattern recognizer, which may produce an output data structure indicating an anticipated bearing fault state (whenever an input state of a high temperature is recognized). A wide range of measurement values and anticipated states may be managed by a state machine, relating to temperature, pressure, vibration, acceleration, momentum, inertia, friction, heat, heat flux, galvanic states, magnetic field states, electrical field states, capacitance states, charge and discharge states, motion, position, and many others. States may comprise combined states, where a data structure includes a series of states, each of which is represented by a place in a byte-like data structure. For example, an industrial machine may be characterized by a genetic structure, such as one that provides pressure, temperature, vibration, and acoustic data, the measurement of which takes one place in the data structure, so that the combined state can be operated on as a byte-like structure, such as a structure for compactly characterizing the current combined state of the machine or environment, or compactly characterizing the anticipated state. This byte-like structure can be used by a state machine for machine learning, such as pattern recognition that operates on the structure to determine patterns that reflect combined effects of multiple conditions. A wide variety of such structure can be tracked and used, such as in machine learning, representing various combinations, of various length, of the different elements that can be sensed in an industrial environment. In embodiments, byte-like structures can be used in a genetic programming technique, such as by substituting different types of data, or data from varying sources, and tracking outcomes over time, so that one or more favorable structures emerges based on the success of those structures when used in real world situations, such as indicating successful predictions of anticipated states, or achievement of success operational outcomes, such as increased efficiency, successful routing of information, achieving increased profits, or the like. That is, by varying what data types and sources are used in byte-like structures that are used for machine optimization over time, a genetic programming-based machine learning facility can “evolve” a set of data structures, consisting of a favorable mix of data types (e.g., pressure, temperature, and vibration), from a favorable mix of data sources (e.g., temperature is derived from sensor X, while vibration comes from sensor Y), for a given purpose. Different desired outcomes may result in different data structures that are best adapted to support effective achievement of those outcomes over time with application of machine learning and promotion of structures with favorable results for the desired outcome in question by genetic programming. The promoted data structures may provide compact, efficient data for various activities as described throughout this disclosure, including being stored in data pools (which may be optimized by storing favorable data structures that provide the best operational results for a given environment), being presented in data marketplaces (such as being presented as the most effective structures for a given purpose), and the like.

[0353] In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, the host processing system 112, such as disposed in the cloud, may include the state system 4020, which may be used to infer or calculate a current state or to determine an anticipated future state relating to the data collection system 102 or some aspect of the environment in which the data collection system 102 is disposed, such as the state of a machine, a component, a workflow, a process, an event (e.g., whether the event has occurred), an object, a person, a condition, a function, or the like. Maintaining state information allows the host processing system 112 to undertake analysis, such as in one or more analytic systems 4018, to determine contextual information, to apply semantic and conditional logic, and perform many other functions as enabled by the processing architecture 4024 described throughout this disclosure.

[0354] In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, the platform 100 includes (or is integrated with, or included in) the host processing system 112, such as on a cloud platform, a policy automation engine 4032 for automating creation, deployment, and management of policies to IoT devices. Polices, which may include access policies, network usage policies, storage usage policies, bandwidth usage policies, device connection policies, security policies, rule-based policies, role-based polices, and others, may be required to govern the use of IoT devices. For example, as IoT devices may have many different network and data communications to other devices, policies may be needed to indicate to what devices a given device can connect, what data can be passed on, and what data can be received. As billions of devices with countless potential connections are expected to be deployed in the near future, it becomes impossible for humans to configure policies for IoT devices on a connection-by-connection basis. Accordingly, an intelligent policy automation engine 4032 may include cognitive features for creating, configuring, and managing policies. The policy automation engine 4032 may consume information about possible policies, such as from a policy database or library, which may include one or more public sources of available policies. These may be written in one or more conventional policy languages or scripts. The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as a machine for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation, thereby avoiding a remote “takeover” by a hacker. This may be accomplished in turn by automatically finding and applying security policies that bar connection of the control infrastructure of the machine to the Internet, by requiring access authentication, or the like. The policy automation engine 4032 may include cognitive features, such as varying the application of policies, the configuration of policies, and the like (such as features based on state information from the state system 4020). The policy automation engine 4032 may take feedback, as from the learning feedback system 4012, such as based on one or more analytic results from the analytic system 4018, such as based on overall system results (such as the extent of security breaches, policy violations, and the like), local results, and analytic results. By variation and selection based on such feedback, the policy automation engine 4032 can, over time, learn to automatically create, deploy, configure, and manage policies across very large numbers of devices, such as managing policies for configuration of connections among IoT devices.

[0355] Methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial IoT devices, including on-device sensor fusion and data storage for an industrial IoT device, where data from multiple sensors is multiplexed at the device for storage of a fused data stream. For example, pressure and temperature data may be multiplexed into a data stream that combines pressure and temperature in a time series, such as in a byte-like structure (where time, pressure, and temperature are bytes in a data structure, so that pressure and temperature remain linked in time, without requiring separate processing of the streams by outside systems), or by adding, dividing, multiplying, subtracting, or the like, such that the fused data can be stored on the device. Any of the sensor data types described throughout this disclosure can be fused in this manner and stored in a local data pool, in storage, or on an IoT device, such as a data collector, a component of a machine, or the like.

[0356] In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a cognitive system is used for a self-organizing storage system 4028 for the data collection system 102. Sensor data, and in particular analog sensor data, can consume large amounts of storage capacity, in particular where a data collection system 102 has multiple sensor inputs onboard or from the local environment. Simply storing all the data indefinitely is not typically a favorable option, and even transmitting all of the data may strain bandwidth limitations, exceed bandwidth permissions (such as exceeding cellular data plan capacity), or the like. Accordingly, storage strategies are needed. These typically include capturing only portions of the data (such as snapshots), storing data for limited time periods, storing portions of the data (such as intermediate or abstracted forms), and the like. With many possible selections among these and other options, determining the correct storage strategy may be highly complex. In embodiments, the self-organizing storage system 4028 may use a cognitive system, based on learning feedback 4012, and use various metrics from the analytic system 4018 or other system of the host cognitive input selection system 4014, such as overall system metrics, analytic metrics, and local performance indicators. The self-organizing storage system 4028 may automatically vary storage parameters, such as storage locations (including local storage on the data collection system 102, storage on nearby data collection systems 102 (such as using peer-to-peer organization) and remote storage, such as network-based storage), storage amounts, storage duration, type of data stored (including individual sensors or input sources 116, as well as various combined or multiplexed data, such as selected under the cognitive input selection systems 4004, 4014), storage type (such as using RAM, Flash, or other short-term memory versus available hard drive space), storage organization (such as in raw form, in hierarchies, and the like), and others. Variation of the parameters may be undertaken with feedback, so that over time the data collection system 102 adapts its storage of data to optimize itself to the conditions of its environment, such as a particular industrial environment, in a way that results in its storing the data that is needed in the right amounts and of the right type for availability to users.

[0357] In embodiments, the local cognitive input selection system 4004 may organize fusion of data for various onboard sensors, external sensors (such as in the local environment) and other input sources 116 to the local data collection system 102 into one or more fused data streams, such as using the multiplexer 4002 to create various signals that represent combinations, permutations, mixes, layers, abstractions, data-metadata combinations, and the like of the source analog and/or digital data that is handled by the data collection system 102. The selection of a particular fusion of sensors may be determined locally by the cognitive input selection system 4004, such as based on learning feedback from the learning feedback system 4012, such as various overall system, analytic system and local system results and metrics. In embodiments, the system may learn to fuse particular combinations and permutations of sensors, such as in order to best achieve correct anticipation of state, as indicated by feedback of the analytic system 4018 regarding its ability to predict future states, such as the various states handled by the state system 4020. For example, the input selection system 4004 may indicate selection of a sub-set of sensors among a larger set of available sensors, and the inputs from the selected sensors may be combined, such as by placing input from each of them into a byte of a defined, multi-bit data structure (such as a combination by taking a signal from each at a given sampling rate or time and placing the result into the byte structure, then collecting and processing the bytes over time), by multiplexing in the multiplexer 4002, such as a combination by additive mixing of continuous signals, and the like. Any of a wide range of signal processing and data processing techniques for combination and fusing may be used, including convolutional techniques, coercion techniques, transformation techniques, and the like. The particular fusion in question may be adapted to a given situation by cognitive learning, such as by having the cognitive input selection system 4004 learn, based on feedback 4012 from results (such as feedback conveyed by the analytic system 4018), such that the local data collection system 102 executes context-adaptive sensor fusion.

[0358] In embodiments, the analytic system 4018 may apply to any of a wide range of analytic techniques, including statistical and econometric techniques (such as linear regression analysis, use similarity matrices, heat map based techniques, and the like), reasoning techniques (such as Bayesian reasoning, rule-based reasoning, inductive reasoning, and the like), iterative techniques (such as feedback, recursion, feed-forward and other techniques), signal processing techniques (such as Fourier and other transforms), pattern recognition techniques (such as Kalman and other filtering techniques), search techniques, probabilistic techniques (such as random walks, random forest algorithms, and the like), simulation techniques (such as random walks, random forest algorithms, linear optimization and the like), and others. This may include computation of various statistics or measures. In embodiments, the analytic system 4018 may be disposed, at least in part, on a data collection system 102, such that a local analytic system can calculate one or more measures, such as measures relating to any of the items noted throughout this disclosure. For example, measures of efficiency, power utilization, storage utilization, redundancy, entropy, and other factors may be calculated onboard, so that the data collection system 102 can enable various cognitive and learning functions noted throughout this disclosure without dependence on a remote (e.g., cloud-based) analytic system.

[0359] In embodiments, the host processing system 112, a data collection system 102, or both, may include, connect to, or integrate with, a self-organizing networking system 4030, which may comprise a cognitive system for providing machine-based, intelligent or organization of network utilization for transport of data in a data collection system, such as for handling analog and other sensor data, or other source data, such as among one or more local data collection systems 102 and a host system 112. This may include organizing network utilization for source data delivered to data collection systems, for feedback data, such as analytic data provided to or via a learning feedback system 4012, data for supporting a marketplace (such as described in connection with other embodiments), and output data provided via output interfaces and ports 4010 from one or more data collection systems 102.

[0360] Methods and systems are disclosed herein for a self-organizing data marketplace for industrial IoT data, including where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success. A marketplace may be set up initially to make available data collected from one or more industrial environments, such as presenting data by type, by source, by environment, by machine, by one or more patterns, or the like (such as in a menu or hierarchy). The marketplace may vary the data collected, the organization of the data, the presentation of the data (including pushing the data to external sites, providing links, configuring APIs by which the data may be accessed, and the like), the pricing of the data, or the like, such as under machine learning, which may vary different parameters of any of the foregoing. The machine learning facility may manage all of these parameters by self-organization, such as by varying parameters over time (including by varying elements of the data types presented), the data sourced used to obtain each type of data, the data structures presented (such as byte-like structures, fused or multiplexed structures (such as representing multiple sensor types), and statistical structures (such as representing various mathematical products of sensor information), among others), the pricing for the data, where the data is presented, how the data is presented (such as by APIs, by links, by push messaging, and the like), how the data is stored, how the data is obtained, and the like. As parameters are varied, feedback may be obtained as to measures of success, such as number of views, yield (e.g., price paid) per access, total yield, per unit profit, aggregate profit, and many others, and the self-organizing machine learning facility may promote configurations that improve measures of success and demote configurations that do not, so that, over time, the marketplace is progressively configured to present favorable combinations of data types (e.g., those that provide robust prediction of anticipated states of particular industrial environments of a given type), from favorable sources (e.g., those that are reliable, accurate and low priced), with effective pricing (e.g., pricing that tends to provide high aggregate profit from the marketplace). The marketplace may include spiders, web crawlers, and the like to seek input data sources, such as finding data pools, connected IoT devices, and the like that publish potentially relevant data. These may be trained by human users and improved by machine learning in a manner similar to that described elsewhere in this disclosure.

[0361] In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data. Referring to FIGS. 15-17, in embodiments, a platform is provided having a cognitive data marketplace 4102, referred to in some cases as a self-organizing data marketplace, for data collected by one or more data collection systems 102 or for data from other sensors or input sources 116 that are located in various data collection environments, such as industrial environments. In addition to data collection systems 102, this may include data collected, handled or exchanged by IoT devices, such as cameras, monitors, embedded sensors, mobile devices, diagnostic devices and systems, instrumentation systems, telematics systems, and the like, such as for monitoring various parameters and features of machines, devices, components, parts, operations, functions, conditions, states, events, workflows and other elements (collectively encompassed by the term “states”) of such environments. Data may also include metadata about any of the foregoing, such as describing data, indicating provenance, indicating elements relating to identity, access, roles, and permissions, providing summaries or abstractions of data, or otherwise augmenting one or more items of data to enable further processing, such as for extraction, transforming, loading, and processing data. Such data (such term including metadata except where context indicates otherwise) may be highly valuable to third parties, either as an individual element (such as the instance where data about the state of an environment can be used as a condition within a process) or in the aggregate (such as the instance where collected data, optionally over many systems and devices in different environments can be used to develop models of behavior, to train learning systems, or the like). As billions of IoT devices are deployed, with countless connections, the amount of available data will proliferate. To enable access and utilization of data, the cognitive data marketplace 4102 enables various components, features, services, and processes for enabling users to supply, find, consume, and transact in packages of data, such as batches of data, streams of data (including event streams), data from various data pools 4120, and the like. In embodiments, the cognitive data marketplace 4102 may be included in, connected to, or integrated with, one or more other components of a host processing architecture 4024 of a host processing system 112, such as a cloud-based system, as well as to various sensors, input sources 116, data collection systems 102 and the like. The cognitive data marketplace 4102 may include marketplace interfaces 4108, which may include one or more supplier interfaces by which data suppliers may make data available and one more consumer interfaces by which data may be found and acquired. The consumer interface may include an interface to a data market search system 4118, which may include features that enable a user to indicate what types of data a user wishes to obtain, such as by entering keywords in a natural language search interface that characterize data or metadata. The search interface can use various search and filtering techniques, including keyword matching, collaborative filtering (such as using known preferences or characteristics of the consumer to match to similar consumers and the past outcomes of those other consumers), ranking techniques (such as ranking based on success of past outcomes according to various metrics, such as those described in connection with other embodiments in this disclosure). In embodiments, a supply interface may allow an owner or supplier of data to supply the data in one or more packages to and through the cognitive data marketplace 4102, such as packaging batches of data, streams of data, or the like. The supplier may pre-package data, such as by providing data from a single input source 116, a single sensor, and the like, or by providing combinations, permutations, and the like (such as multiplexed analog data, mixed bytes of data from multiple sources, results of extraction, loading and transformation, results of convolution, and the like), as well as by providing metadata with respect to any of the foregoing. Packaging may include pricing, such as on a per-batch basis, on a streaming basis (such as subscription to an event feed or other feed or stream), on a per item basis, on a revenue share basis, or other basis. For data involving pricing, a data transaction system 4114 may track orders, delivery, and utilization, including fulfillment of orders. The transaction system 4114 may include rich transaction features, including digital rights management, such as by managing cryptographic keys that govern access control to purchased data, that govern usage (such as allowing data to be used for a limited time, in a limited domain, by a limited set of users or roles, or for a limited purpose). The transaction system 4114 may manage payments, such as by processing credit cards, wire transfers, debits, and other forms of consideration.

[0362] In embodiments, a cognitive data packaging system 4110 of the marketplace 4102 may use machine-based intelligence to package data, such as by automatically configuring packages of data in batches, streams, pools, or the like. In embodiments, packaging may be according to one or more rules, models, or parameters, such as by packaging or aggregating data that is likely to supplement or complement an existing model. For example, operating data from a group of similar machines (such as one or more industrial machines noted throughout this disclosure) may be aggregated together, such as based on metadata indicating the type of data or by recognizing features or characteristics in the data stream that indicate the nature of the data. In embodiments, packaging may occur using machine learning and cognitive capabilities, such as by learning what combinations, permutations, mixes, layers, and the like of input sources 116, sensors, information from data pools 4120 and information from data collection systems 102 are likely to satisfy user requirements or result in measures of success. Learning may be based on learning feedback 4012, such as learning based on measures determined in an analytic system 4018, such as system performance measures, data collection measures, analytic measures, and the like. In embodiments, success measures may be correlated to marketplace success measures, such as viewing of packages, engagement with packages, purchase or licensing of packages, payments made for packages, and the like. Such measures may be calculated in an analytic system 4018, including associating particular feedback measures with search terms and other inputs, so that the cognitive data packaging system 4110 can find and configure packages that are designed to provide increased value to consumers and increased returns for data suppliers. In embodiments, the cognitive data packaging system 4110 can automatically vary packaging, such as using different combinations, permutations, mixes, and the like, and varying weights applied to given input sources, sensors, data pools and the like, using learning feedback 4012 to promote favorable packages and de-emphasize less favorable packages. This may occur using genetic programming and similar techniques that compare outcomes for different packages. Feedback may include state information from the state system 4020 (such as about various operating states, and the like), as well as about marketplace conditions and states, such as pricing and availability information for other data sources. Thus, an adaptive cognitive data packaging system 4110 is provided that automatically adapts to conditions to provide favorable packages of data for the marketplace 4102.

[0363] In embodiments, a cognitive data pricing system 4112 may be provided to set pricing for data packages. In embodiments, the data pricing system 4112 may use a set of rules, models, or the like, such as setting pricing based on supply conditions, demand conditions, pricing of various available sources, and the like. For example, pricing for a package may be configured to be set based on the sum of the prices of constituent elements (such as input sources, sensor data, or the like), or to be set based on a rule-based discount to the sum of prices for constituent elements, or the like. Rules and conditional logic may be applied, such as rules that factor in cost factors (such as bandwidth and network usage, peak demand factors, scarcity factors, and the like), rules that factor in utilization parameters (such as the purpose, domain, user, role, duration, or the like for a package) and many others. In embodiments, the cognitive data pricing system 4112 may include fully cognitive, intelligent features, such as using genetic programming including automatically varying pricing and tracking feedback on outcomes. Outcomes on which tracking feedback may be based include various financial yield metrics, utilization metrics and the like that may be provided by calculating metrics in an analytic system 4018 on data from the data transaction system 4114.

[0364] Methods and systems are disclosed herein for self-organizing data pools which may include self-organization of data pools based on utilization and/or yield metrics, including utilization and/or yield metrics that are tracked for a plurality of data pools. The data pools may initially comprise unstructured or loosely structured pools of data that contain data from industrial environments, such as sensor data from or about industrial machines or components. For example, a data pool might take streams of data from various machines or components in an environment, such as turbines, compressors, batteries, reactors, engines, motors, vehicles, pumps, rotors, axles, bearings, valves, and many others, with the data streams containing analog and/or digital sensor data (of a wide range of types), data published about operating conditions, diagnostic and fault data, identifying data for machines or components, asset tracking data, and many other types of data. Each stream may have an identifier in the pool, such as indicating its source, and optionally its type. The data pool may be accessed by external systems, such as through one or more interfaces or APIs (e.g., RESTful APIs), or by data integration elements (such as gateways, brokers, bridges, connectors, or the like), and the data pool may use similar capabilities to get access to available data streams. A data pool may be managed by a self-organizing machine learning facility, which may configure the data pool, such as by managing what sources are used for the pool, managing what streams are available, and managing APIs or other connections into and out of the data pool. The self-organization may take feedback such as based on measures of success that may include measures of utilization and yield. The measures of utilization and yield that may include may account for the cost of acquiring and/or storing data, as well as the benefits of the pool, measured either by profit or by other measures that may include user indications of usefulness, and the like. For example, a self-organizing data pool might recognize that chemical and radiation data for an energy production environment are regularly accessed and extracted, while vibration and temperature data have not been used, in which case the data pool might automatically reorganize, such as by ceasing storage of vibration and/or temperature data, or by obtaining better sources of such data. This automated reorganization can also apply to data structures, such as promoting different data types, different data sources, different data structures, and the like, through progressive iteration and feedback.

[0365] In embodiments, a platform is provided having self-organization of data pools based on utilization and/or yield metrics. In embodiments, the data pools 4120 may be self-organizing data pools 4120, such as being organized by cognitive capabilities as described throughout this disclosure. The data pools 4120 may self-organize in response to learning feedback 4012, such as based on feedback of measures and results, including calculated in an analytic system 4018. Organization may include determining what data or packages of data to store in a pool (such as representing particular combinations, permutations, aggregations, and the like), the structure of such data (such as in flat, hierarchical, linked, or other structures), the duration of storage, the nature of storage media (such as hard disks, flash memory, SSDs, network-based storage, or the like), the arrangement of storage bits, and other parameters. The content and nature of storage may be varied, such that a data pool 4120 may learn and adapt, such as based on states of the host system 112, one or more data collection systems 102, storage environment parameters (such as capacity, cost, and performance factors), data collection environment parameters, marketplace parameters, and many others. In embodiments, pools 4120 may learn and adapt, such as by variation of the above and other parameters in response to yield metrics (such as return on investment, optimization of power utilization, optimization of revenue, and the like).

[0366] Methods and systems are disclosed herein for training AI models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, and where the AI model operates on sensor data from an industrial environment. As noted above, these models may include operating models for industrial environments, machines, workflows, models for anticipating states, models for predicting fault and optimizing maintenance, models for self-organizing storage (on devices, in data pools and/or in the cloud), models for optimizing data transport (such as for optimizing network coding, network-condition-sensitive routing, and the like), models for optimizing data marketplaces, and many others.

[0367] In embodiments, a platform is provided having training AI models based on industry-specific feedback. In embodiments, the various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources 116 (such as relating to optimization of specific machines, devices, components, processes, and the like). Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features (such as for a marketplace 4102 or for other purposes of the host processing system 112) may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application involving IoT devices (such as an industrial environment). This may include optimization of efficiency (such as in electrical, electromechanical, magnetic, physical, thermodynamic, chemical and other processes and systems), optimization of outputs (such as for production of energy, materials, products, services and other outputs), prediction, avoidance and mitigation of faults (such as in the aforementioned systems and processes), optimization of performance measures (such as returns on investment, yields, profits, margins, revenues and the like), reduction of costs (including labor costs, bandwidth costs, data costs, material input costs, licensing costs, and many others), optimization of benefits (such as relating to safety, satisfaction, health), optimization of workflows (such as optimizing time and resource allocation to processes), and others.

[0368] Methods and systems are disclosed herein for a self-organized swarm of industrial data collectors, including a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm. Each member of the swarm may be configured with intelligence, and the ability to coordinate with other members. For example, a member of the swarm may track information about what data other members are handling, so that data collection activities, data storage, data processing, and data publishing can be allocated intelligently across the swarm, taking into account conditions of the environment, capabilities of the members of the swarm, operating parameters, rules (such as from a rules engine that governs the operation of the swarm), and current conditions of the members. For example, among four collectors, one that has relatively low current power levels (such as a low battery), might be temporarily allocated the role of publishing data, because it may receive a dose of power from a reader or interrogation device (such as an RFID reader) when it needs to publish the data. A second collector with good power levels and robust processing capability might be assigned more complex functions, such as processing data, fusing data, organizing the rest of the swarm (including self-organization under machine learning, such that the swarm is optimized over time, including by adjusting operating parameters, rules, and the like based on feedback), and the like. A third collector in the swarm with robust storage capabilities might be assigned the task of collecting and storing a category of data, such as vibration sensor data, that consumes considerable bandwidth. A fourth collector in the swarm, such as one with lower storage capabilities, might be assigned the role of collecting data that can usually be discarded, such as data on current diagnostic conditions, where only data on faults needs to be maintained and passed along. Members of a swarm may connect by peer-to-peer relationships by using a member as a “master” or “hub,” or by having them connect in a series or ring, where each member passes along data (including commands) to the next, and is aware of the nature of the capabilities and commands that are suitable for the preceding and/or next member. The swarm may be used for allocation of storage across it (such as using memory of each memory as an aggregate data store. In these examples, the aggregate data store may support a distributed ledger, which may store transaction data, such as for transactions involving data collected by the swarm, transactions occurring in the industrial environment, or the like. In embodiments, the transaction data may also include data used to manage the swarm, the environment, or a machine or components thereof. The swarm may self-organize, either by machine learning capability disposed on one or more members of the swarm, or based on instructions from an external machine learning facility, which may optimize storage, data collection, data processing, data presentation, data transport, and other functions based on managing parameters that are relevant to each. The machine learning facility may start with an initial configuration and vary parameters of the swarm relevant to any of the foregoing (also including varying the membership of the swarm), such as iterating based on feedback to the machine learning facility regarding measures of success (such as utilization measures, efficiency measures, measures of success in prediction or anticipation of states, productivity measures, yield measures, profit measures, and others). Over time, the swarm may be optimized to a favorable configuration to achieve the desired measure of success for an owner, operator, or host of an industrial environment or a machine, component, or process thereof.

[0369] The swarm 4202 may be organized based on a hierarchical organization (such as where a master data collection system 102 organizes and directs activities of one or more subservient data collection systems 102), a collaborative organization (such as where decision-making for the organization of the swarm 4202 is distributed among the data collectors 102 (such as using various models for decision-making, such as voting systems, points systems, least-cost routing systems, prioritization systems, and the like), and the like.) In embodiments, one or more of the data collectors 102 may have mobility capabilities, such as in cases where a data collector is disposed on or in a mobile robot, drone, mobile submersible, or the like, so that organization may include the location and positioning of the data collectors 102. Data collection systems 102 may communicate with each other and with the host processing system 112, including sharing an aggregate allocated storage space involving storage on or accessible to one or more of the collectors (which in embodiment may be treated as a unified storage space even if physically distributed, such as using virtualization capabilities). Organization may be automated based on one or more rules, models, conditions, processes, or the like (such as embodied or executed by conditional logic), and organization may be governed by policies, such as handled by the policy engine. Rules may be based on industry, application- and domain-specific objects, classes, events, workflows, processes, and systems, such as by setting up the swarm 4202 to collect selected types of data at designated places and times, such as coordinated with the foregoing. For example, the swarm 4202 may assign data collectors 102 to serially collect diagnostic, sensor, instrumentation and/or telematic data from each of a series of machines that execute an industrial process (such as a robotic manufacturing process), such as at the time and location of the input to and output from each of those machines. In embodiments, self-organization may be cognitive, such as where the swarm varies one or more collection parameters and adapts the selection of parameters, weights applied to the parameters, or the like, over time. In examples, this may be in response to learning and feedback, such as from the learning feedback system 4012 that may be based on various feedback measures that may be determined by applying the analytic system 4018 (which in embodiments may reside on the swarm 4202, the host processing system 112, or a combination thereof) to data handled by the swarm 4202 or to other elements of the various embodiments disclosed herein (including marketplace elements and others). Thus, the swarm 4202 may display adaptive behavior, such as adapting to the current state 4020 or an anticipated state of its environment (accounting for marketplace behavior), behavior of various objects (such as IoT devices, machines, components, and systems), processes (including events, states, workflows, and the like), and other factors at a given time. Parameters that may be varied in a process of variation (such as in a neural net, self-organizing map, or the like), selection, promotion, or the like (such as those enabled by genetic programming or other AI-based techniques). Parameters that may be managed, varied, selected and adapted by cognitive, machine learning may include storage parameters (location, type, duration, amount, structure and the like across the swarm 4202), network parameters (such as how the swarm 4202 is organized, such as in mesh, peer-to-peer, ring, serial, hierarchical and other network configurations as well as bandwidth utilization, data routing, network protocol selection, network coding type, and other networking parameters), security parameters (such as settings for various security applications and services), location and positioning parameters (such as routing movement of mobile data collectors 102 to locations, positioning and orienting collectors 102 and the like relative to points of data acquisition, relative to each other, and relative to locations where network availability may be favorable, among others), input selection parameters (such as input selection among sensors, input sources 116 and the like for each collector 102 and for the aggregate collection), data combination parameters (such as those for sensor fusion, input combination, multiplexing, mixing, layering, convolution, and other combinations), power parameters (such as parameters based on power levels and power availability for one or more collectors 102 or other objects, devices, or the like), states (including anticipated states and conditions of the swarm 4202, individual collection systems 102, the host processing system 112 or one or more objects in an environment), events, and many others. Feedback may be based on any of the kinds of feedback described herein, such that over time the swarm may adapt to its current and anticipated situation to achieve a wide range of desired objectives.

[0370] Methods and systems are disclosed herein for an industrial IoT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial IoT data. A distributed ledger may distribute storage across devices, using a secure protocol, such as those used for cryptocurrencies (such as the Blockchain™ protocol used to support the Bitcoin™ currency). A ledger or similar transaction record, which may comprise a structure where each successive member of a chain stores data for previous transactions, and a competition can be established to determine which of alternative data stored data structures is “best” (such as being most complete), can be stored across data collectors, industrial machines or components, data pools, data marketplaces, cloud computing elements, servers, and/or on the IT infrastructure of an enterprise (such as an owner, operator or host of an industrial environment or of the systems disclosed herein). The ledger or transaction may be optimized by machine learning, such as to provide storage efficiency, security, redundancy, or the like.

[0371] In embodiments, the cognitive data marketplace 4102 may use a secure architecture for tracking and resolving transactions, such as a distributed ledger 4104, wherein transactions in data packages are tracked in a chained, distributed data structure, such as a Blockchain™, allowing forensic analysis and validation where individual devices store a portion of the ledger representing transactions in data packages. The distributed ledger 4104 may be distributed to IoT devices, to data pools 4120, to data collection systems 102, and the like, so that transaction information can be verified without reliance on a single, central repository of information. The transaction system 4114 may be configured to store data in the distributed ledger 4104 and to retrieve data from it (and from constituent devices) in order to resolve transactions. Thus, a distributed ledger 4104 for handling transactions in data, such as for packages of IoT data, is provided. In embodiments, the self-organizing storage system 4028 may be used for optimizing storage of distributed ledger data, as well as for organizing storage of packages of data, such as IoT data, that can be presented in the marketplace 4102.

[0372] Methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing and/or other network conditions. Network sensitivity can include awareness of the price of data transport (such as allowing the system to pull or push data during off-peak periods or within the available parameters of paid data plans), the quality of the network (such as to avoid periods where errors are likely), the quality of environmental conditions (such as delaying transmission until signal quality is good, such as when a collector emerges from a shielded environment, avoiding wasting use of power when seeking a signal when shielded, such as by large metal structures typically of industrial environments), and the like.

[0373] Methods and systems are disclosed herein for a remotely organized universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment. For example, interfaces can recognize what sensors are available and interfaces and/or processors can be turned on to take input from such sensors, including hardware interfaces that allow the sensors to plug in to the data collector, wireless data interfaces (such as where the collector can ping the sensor, optionally providing some power via an interrogation signal), and software interfaces (such as for handling particular types of data). Thus, a collector that is capable of handling various kinds of data can be configured to adapt to the particular use in a given environment. In embodiments, configuration may be automatic or under machine learning, which may improve configuration by optimizing parameters based on feedback measures over time.

[0374] Methods and systems are disclosed herein for self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data. Self-organizing storage may allocate storage based on application of machine learning, which may improve storage configuration based on feedback measure over time. Storage may be optimized by configuring what data types are used (e.g., byte-like structures, structures representing fused data from multiple sensors, structures representing statistics or measures calculated by applying mathematical functions on data, and the like), by configuring compression, by configuring data storage duration, by configuring write strategies (such as by striping data across multiple storage devices, using protocols where one device stores instructions for other devices in a chain, and the like), and by configuring storage hierarchies, such as by providing pre-calculated intermediate statistics to facilitate more rapid access to frequently accessed data items. Thus, highly intelligent storage systems may be configured and optimized, based on feedback, over time.

[0375] Methods and systems are disclosed herein for self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment. Network coding, including random linear network coding, can enable highly efficient and reliable transport of large amounts of data over various kinds of networks. Different network coding configurations can be selected, based on machine learning, to optimize network coding and other network transport characteristics based on network conditions, environmental conditions, and other factors, such as the nature of the data being transported, environmental conditions, operating conditions, and the like (including by training a network coding selection model over time based on feedback of measures of success, such as any of the measures described herein).

[0376] In embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network. A cognitive system may vary one or more parameters for networking, such as network type selection (e.g., selecting among available local, cellular, satellite, Wi-Fi, Bluetooth™, NFC, Zigbee® and other networks), network selection (such as selecting a specific network, such as one that is known to have desired security features), network coding selection (such as selecting a type of network coding for efficient transport [such as random linear network coding, fixed coding, and others]), network timing selection (such as configuring delivery based on network pricing conditions, traffic and the like), network feature selection (such as selecting cognitive features, security features, and the like), network conditions (such as network quality based on current environmental or operation conditions), network feature selection (such as enabling available authentication, permission and similar systems), network protocol selection (such as among HTTP, IP, TCP/IP, cellular, satellite, serial, packet, streaming, and many other protocols), and others. Given bandwidth constraints, price variations, sensitivity to environmental factors, security concerns, and the like, selecting the optimal network configuration can be highly complex and situation dependent. The self-organizing networking system 4030 may vary combinations and permutations of these parameters while taking input from a learning feedback system 4012 such as using information from the analytic system 4018 about various measures of outcomes. In the many examples, outcomes may include overall system measures, analytic success measures, and local performance indicators. In embodiments, input from a learning feedback system 4012 may include information from various sensors and input sources 116, information from the state system 4020 about states (such as events, environmental conditions, operating conditions, and many others, or other information) or taking other inputs. By variation and selection of alternative configurations of networking parameters in different states, the self-organizing networking system may find configurations that are well-adapted to the environment that is being monitored or controlled by the host system 112, such as the instance where one or more data collection systems 102 are located and that are well-adapted to emerging network conditions. Thus, a self-organizing, network-condition-adaptive data collection system is provided.

[0377] Referring to FIG. 42, a data collection system 102 may have one or more output interfaces and/or ports 4010. These may include network ports and connections, application programming interfaces, and the like. Methods and systems are disclosed herein for a haptic or multi-sensory user interface, including a wearable haptic or multi-sensory user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. For example, an interface may, based on a data structure configured to support the interface, be set up to provide a user with input or feedback, such as based on data from sensors in the environment. For example, if a fault condition based on a vibration data (such as resulting from a bearing being worn down, an axle being misaligned, or a resonance condition between machines) is detected, it can be presented in a haptic interface by vibration of an interface, such as shaking a wrist-worn device. Similarly, thermal data indicating overheating could be presented by warming or cooling a wearable device, such as while a worker is working on a machine and cannot necessarily look at a user interface. Similarly, electrical or magnetic data may be presented by a buzzing, and the like, such as to indicate presence of an open electrical connection or wire, etc. That is, a multi-sensory interface can intuitively help a user (such as a user with a wearable device) get a quick indication of what is going on in an environment, with the wearable interface having various modes of interaction that do not require a user to have eyes on a graphical UI, which may be difficult or impossible in many industrial environments where a user needs to keep an eye on the environment.

[0378] In embodiments, a platform is provided having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a haptic user interface 4302 is provided as an output for a data collection system 102, such as a system for handling and providing information for vibration, heat, electrical, and/or sound outputs, such as to one or more components of the data collection system 102 or to another system, such as a wearable device, mobile phone, or the like. A data collection system 102 may be provided in a form factor suitable for delivering haptic input to a user, such as vibration, warming or cooling, buzzing, or the like, such as input disposed in headgear, an armband, a wristband or watch, a belt, an item of clothing, a uniform, or the like. In such cases, data collection systems 102 may be integrated with gear, uniforms, equipment, or the like worn by users, such as individuals responsible for operating or monitoring an industrial environment. In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may trigger haptic feedback. For example, if a nearby industrial machine is overheating, the haptic interface may alert a user by warming up, or by sending a signal to another device (such as a mobile phone) to warm up. If a system is experiencing unusual vibrations, the haptic interface may vibrate. Thus, through various forms of haptic input, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors (such as those in an industrial environment) without requiring them to read messages or divert their visual attention away from the task at hand. The haptic interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the haptic system 4202. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitive haptic system may be provided, where selection of inputs or triggers for haptic feedback, selection of outputs, timing, intensity levels, durations, and other parameters (or weights applied to them) may be varied in a process of variation, promotion, and selection (such as using genetic programming) with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive haptic interface for a data collection system 102 is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.

[0379] Methods and systems are disclosed herein for a presentation layer for AR/VR industrial glasses, where heat map elements are presented based on patterns and/or parameters in collected data. Methods and systems are disclosed herein for condition-sensitive, self-organized tuning of AR/VR interfaces based on feedback metrics and/or training in industrial environments. In embodiments, any of the data, measures, and the like described throughout this disclosure can be presented by visual elements, overlays, and the like for presentation in the AR/VR interfaces, such as in industrial glasses, on AR/VR interfaces on smart phones or tablets, on AR/VR interfaces on data collectors (which may be embodied in smart phones or tablets), on displays located on machines or components, and/or on displays located in industrial environments.

[0380] In embodiments, a platform is provided having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having heat maps 4304 displaying collected data from a data collection system 102 for providing input to an AR/VR interface 4308. In embodiments, the heat map interface 4304 is provided as an output for a data collection system 102, such as for handling and providing information for visualization of various sensor data and other data (such as map data, analog sensor data, and other data), such as to one or more components of the data collection system 102 or to another system, such as a mobile device, tablet, dashboard, computer, AR/VR device, or the like. A data collection system 102 may be provided in a form factor suitable for delivering visual input to a user, such as the presentation of a map that includes indicators of levels of analog and digital sensor data (such as data indicating levels of rotation, vibration, heating or cooling, pressure, and many other conditions). In such cases, data collection systems 102 may be integrated with equipment, or the like that are used by individuals responsible for operating or monitoring an industrial environment. In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to a heat map. Coordinates may include real world location coordinates (such as geo-location or location on a map of an environment), as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow for representation of analog sensor signals, digital signals, input source information, and various combinations, in a map-based visualization, such that colors may represent varying levels of input along the relevant dimensions. For example, if a nearby industrial machine is overheating, the heat map interface may alert a user by showing a machine in bright red. If a system is experiencing unusual vibrations, the heat map interface may show a different color for a visual element for the machine, or it may cause an icon or display element representing the machine to vibrate in the interface, calling attention to the element. Clicking, touching, or otherwise interacting with the map can allow a user to drill down and see underlying sensor or input data that is used as an input to the heat map display. Thus, through various forms of display, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors, such as those in an industrial environment, without requiring them to read text-based messages or input. The heat map interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the heat map UI 4304. This may include rule-based or model-based feedback (such as feedback providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitive heat map system may be provided, where selection of inputs or triggers for heat map displays, selection of outputs, colors, visual representation elements, timing, intensity levels, durations and other parameters (or weights applied to them) may be varied in a process of variation, promotion and selection (such as selection using genetic programming) with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive heat map interface for a data collection system 102, or data collected thereby 102, or data handled by a host processing system 112, is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior and reaction, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.

[0381] In embodiments, a platform is provided having automatically tuned AR/VR visualization of data collected by a data collector. In embodiments, a platform is provided having an automatically tuned AR/VR visualization system 4308 for visualization of data collected by a data collection system 102, such as the case where the data collection system 102 has an AR/VR interface 4308 or provides input to an AR/VR interface 4308 (such as a mobile phone positioned in a virtual reality or AR headset, a set of AR glasses, or the like). In embodiments, the AR/VR system 4308 is provided as an output interface of a data collection system 102, such as a system for handling and providing information for visualization of various sensor data and other data (such as map data, analog sensor data, and other data), such as to one or more components of the data collection system 102 or to another system, such as a mobile device, tablet, dashboard, computer, AR/VR device, or the like. A data collection system 102 may be provided in a form factor suitable for delivering AR or VR visual, auditory, or other sensory input to a user, such as by presenting one or more displays such as 3D-realistic visualizations, objects, maps, camera overlays, or other overlay elements, maps and the like that include or correspond to indicators of levels of analog and digital sensor data (such as data indicating levels of rotation, vibration, heating or cooling, pressure and many other conditions, to input sources 116, or the like). In such cases, data collection systems 102 may be integrated with equipment, or the like that are used by individuals responsible for operating or monitoring an industrial environment.

[0382] In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to populate, configure, modify, or otherwise determine the AR/VR element. Visual elements may include a wide range of icons, map elements, menu elements, sliders, toggles, colors, shapes, sizes, and the like, for representation of analog sensor signals, digital signals, input source information, and various combinations. In many examples, colors, shapes, and sizes of visual overlay elements may represent varying levels of input along the relevant dimensions for a sensor or combination of sensors. In further examples, if a nearby industrial machine is overheating, an AR element may alert a user by showing an icon representing that type of machine in flashing red color in a portion of the display of a pair of AR glasses. If a system is experiencing unusual vibrations, a virtual reality interface showing visualization of the components of the machine (such as an overlay of a camera view of the machine with 3D visualization elements) may show a vibrating component in a highlighted color, with motion, or the like, to ensure the component stands out in a virtual reality environment being used to help a user monitor or service the machine. Clicking, touching, moving eyes toward, or otherwise interacting with a visual element in an AR/VR interface may allow a user to drilldown and see underlying sensor or input data that is used as an input to the display. Thus, through various forms of display, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors (such as in an industrial environment), without requiring them to read text-based messages or input or divert attention from the applicable environment (whether it is a real environment with AR features or a virtual environment, such as for simulation, training, or the like).

[0383] The AR/VR output interface 4308, and selection and configuration of what outputs or displays should be provided, may be handled in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that AR/VR display signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the AR/VR UI 4308. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitively tuned AR/VR interface control system 4308 may be provided, where selection of inputs or triggers for AR/VR display elements, selection of outputs (such as colors, visual representation elements, timing, intensity levels, durations and other parameters [or weights applied to them]) and other parameters of a VR/AR environment may be varied in a process of variation, promotion and selection (such as the use of genetic programming) with feedback based on real world responses in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive, tuned AR/VR interface for a data collection system 102, or data collected thereby 102, or data handled by a host processing system 112, is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior and reaction, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.

[0384] As noted above, methods and systems are disclosed herein for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility. Embodiments include using continuous ultrasonic monitoring of an industrial environment as a source for a cloud-deployed pattern recognizer. Embodiments include using continuous ultrasonic monitoring to provide updated state information to a state machine that is used as an input to a cloud-deployed pattern recognizer. Embodiments include making available continuous ultrasonic monitoring information to a user based on a policy declared in a policy engine. Embodiments include storing continuous ultrasonic monitoring data with other data in a fused data structure on an industrial sensor device. Embodiments include making a stream of continuous ultrasonic monitoring data from an industrial environment available as a service from a data marketplace. Embodiments include feeding a stream of continuous ultrasonic monitoring data into a self-organizing data pool. Embodiments include training a machine learning model to monitor a continuous ultrasonic monitoring data stream where the model is based on a training set created from human analysis of such a data stream, and is improved based on data collected on performance in an industrial environment.

[0385] Embodiments include a swarm of data collectors that include at least one data collector for continuous ultrasonic monitoring of an industrial environment and at least one other type of data collector. Embodiments include using a distributed ledger to store time-series data from continuous ultrasonic monitoring across multiple devices. Embodiments include collecting a stream of continuous ultrasonic data in a self-organizing data collector, a network-sensitive data collector, a remotely organized data collector, a data collector having self-organized storage and the like. Embodiments include using self-organizing network coding to transport a stream of ultrasonic data collected from an industrial environment. Embodiments include conveying an indicator of a parameter of a continuously collected ultrasonic data stream via an interface where the interface is one of a sensory interface of a wearable device, a heat map visual interface of a wearable device, an interface that operates with self-organized tuning of the interface layer, and the like.

[0386] As noted above, methods and systems are disclosed herein for cloud-based, machine pattern recognition based on fusion of remote analog industrial sensors. Embodiments include taking input from a plurality of analog sensors disposed in an industrial environment, multiplexing the sensors into a multiplexed data stream, feeding the data stream into a cloud-deployed machine learning facility, and training a model of the machine learning facility to recognize a defined pattern associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer on input states from a state machine that characterizes states of an industrial environment. Embodiments include deploying policies by a policy engine that govern what data can be used by what users and for what purpose in cloud-based, machine learning. Embodiments include using a cloud-based platform to identify patterns in data across a plurality of data pools that contain data published from industrial sensors. Embodiments include training a model to identify preferred sensor sets to diagnose a condition of an industrial environment, where a training set is created by a human user and the model is improved based on feedback from data collected about conditions in an industrial environment.

[0387] Embodiments include a swarm of data collectors that is governed by a policy that is automatically propagated through the swarm. Embodiments include using a distributed ledger to store sensor fusion information across multiple devices. Embodiments include feeding input from a set of data collectors into a cloud-based pattern recognizer that uses data from multiple sensors for an industrial environment. The data collectors may be self-organizing data collectors, network-sensitive data collectors, remotely organized data collectors, a set of data collectors having self-organized storage, and the like. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport of data fused from multiple sensors in the environment. Embodiments include conveying information formed by fusing inputs from multiple sensors in an industrial data collection system in an interface such as a multi-sensory interface, a heat map interface, an interface that operates with self-organized tuning of the interface layer, and the like.

[0388] As noted above, methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. Embodiments include using a policy engine to determine what state information can be used for cloud-based machine analysis. Embodiments include feeding inputs from multiple devices that have fused and on-device storage of multiple sensor streams into a cloud-based pattern recognizer to determine an anticipated state of an industrial environment. Embodiments include making an output, such as anticipated state information, from a cloud-based machine pattern recognizer that analyzes fused data from remote, analog industrial sensors available as a data service in a data marketplace. Embodiments include using a cloud-based pattern recognizer to determine an anticipated state of an industrial environment based on data collected from data pools that contain streams of information from machines in the environment. Embodiments include training a model to identify preferred state information to diagnose a condition of an industrial environment, where a training set is created by a human user and the model is improved based on feedback from data collected about conditions in an industrial environment. Embodiments include a swarm of data collectors that feeds a state machine that maintains current state information for an industrial environment. Embodiments include using a distributed ledger to store historical state information for fused sensor states a self-organizing data collector that feeds a state machine that maintains current state information for an industrial environment. Embodiments include a data collector that feeds a state machine that maintains current state information for an industrial environment where the data collector may be a network sensitive data collector, a remotely organized data collector, a data collector with self-organized storage, and the like. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport and maintains anticipated state information for the environment. Embodiments include conveying anticipated state information determined by machine learning in an industrial data collection system in an interface where the interface may be one or more of a multisensory interface, a heat map interface an interface that operates with self-organized tuning of the interface layer, and the like.

[0389] As noted above, methods and systems are disclosed herein for a cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices, including a cloud-based policy automation engine for IoT, enabling creation, deployment and management of policies that apply to IoT devices. Policies can relate to data usage to an on-device storage system that stores fused data from multiple industrial sensors, or what data can be provided to whom in a self-organizing marketplace for IoT sensor data. Policies can govern how a self-organizing swarm or data collector should be organized for a particular industrial environment, how a network-sensitive data collector should use network bandwidth for a particular industrial environment, how a remotely organized data collector should collect, and make available, data relating to a specified industrial environment, or how a data collector should self-organize storage for a particular industrial environment. Policies can be deployed across a set of self-organizing pools of data that contain data streamed from industrial sensing devices to govern use of data from the pools or stored on a device that governs use of storage capabilities of the device for a distributed ledger. Embodiments include training a model to determine what policies should be deployed in an industrial data collection system. Embodiments include a system for data collection in an industrial environment with a policy engine for deploying policy within the system and, optionally, self-organizing network coding for data transport, wherein in certain embodiments, a policy applies to how data will be presented in a multi-sensory interface, a heat map visual interface, or in an interface that operates with self-organized tuning of the interface layer.

[0390] As noted above, methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial IoT devices, such as an industrial data collector, including self-organizing, remotely organized, or network-sensitive industrial data collectors, where data from multiple sensors is multiplexed at the device for storage of a fused data stream. Embodiments include a self-organizing marketplace that presents fused sensor data that is extracted from on-device storage of IoT devices. Embodiments include streaming fused sensor information from multiple industrial sensors and from an on-device data storage facility to a data pool. Embodiments include training a model to determine what data should be stored on a device in a data collection environment. Embodiments include a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection, where at least some of the data collectors have on-device storage of fused data from multiple sensors. Embodiments include storing distributed ledger information with fused sensor information on an industrial IoT device. Embodiments include a system for data collection with on-device sensor fusion, such as of industrial sensor data and, optionally, self-organizing network coding for data transport, where data structures are stored to support alternative, multi-sensory modes of presentation, visual heat map modes of presentation, and/or an interface that operates with self-organized tuning of the interface layer.

[0391] As noted above, methods and systems are disclosed herein for a self-organizing data marketplace for industrial IoT data, where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success. Embodiments include organizing a set of data pools in a self-organizing data marketplace based on utilization metrics for the data pools. Embodiments include training a model to determine pricing for data in a data marketplace. The data marketplace is fed with data streams from a self-organizing swarm of industrial data collectors, a set of industrial data collectors that have self-organizing storage, or self-organizing, network-sensitive, or remotely organized industrial data collectors. Embodiments include using a distributed ledger to store transactional data for a self-organizing marketplace for industrial IoT data. Embodiments include using self-organizing network coding for data transport to a marketplace for sensor data collected in industrial environments. Embodiments include providing a library of data structures suitable for presenting data in alternative, multi-sensory interface modes in a data marketplace, in heat map visualization, and/or in interfaces that operate with self-organized tuning of the interface layer.

[0392] As noted above, methods and systems are disclosed herein for self-organizing data pools such as those that self-organize based on utilization and/or yield metrics that may be tracked for a plurality of data pools. In embodiments, the pools contain data from self-organizing data collectors. Embodiments include training a model to present the most valuable data in a data marketplace, where training is based on industry-specific measures of success. Embodiments include populating a set of self-organizing data pools with data from a self-organizing swarm of data collectors. Embodiments include using a distributed ledger to store transactional information for data that is deployed in data pools, where the distributed ledger is distributed across the data pools. Embodiments include populating a set of self-organizing data pools with data from a set of network-sensitive or remotely organized data collectors or a set of data collectors having self-organizing storage. Embodiments include a system for data collection in an industrial environment with self-organizing pools for data storage and self-organizing network coding for data transport, such as a system that includes a source data structure for supporting data presentation in a multi-sensory interface, in a heat map interface, and/or in an interface that operates with self-organized tuning of the interface layer.

[0393] As noted above, methods and systems are disclosed herein for training AI models based on industry-specific feedback, such as that reflects a measure of utilization, yield, or impact, where the AI model operates on sensor data from an industrial environment. Embodiments include training a swarm of data collectors, or data collectors, such as remotely organized, self-organizing, or network-sensitive data collectors, based on industry-specific feedback or network and industrial conditions in an industrial environment, such as to configure storage. Embodiments include training an AI model to identify and use available storage locations in an industrial environment for storing distributed ledger information. Embodiments include training a remote organizer for a remotely organized data collector based on industry-specific feedback measures. Embodiments include a system for data collection in an industrial environment with cloud-based training of a network coding model for organizing network coding for data transport or a facility that manages presentation of data in a multi-sensory interface, in a heat map interface, and/or in an interface that operates with self-organized tuning of the interface layer.

[0394] As noted above, methods and systems are disclosed herein for a self-organized swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm. Embodiments include deploying distributed ledger data structures across a swarm of data. Data collectors may be network-sensitive data collectors configured for remote organization or have self-organizing storage. Systems for data collection in an industrial environment with a swarm can include a self-organizing network coding for data transport. Systems include swarms that relay information for use in a multi-sensory interface, in a heat map interface, and/or in an interface that operates with self-organized tuning of the interface layer.

[0395] As noted above, methods and systems are disclosed herein for an industrial IoT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial IoT data. Embodiments include a self-organizing data collector that is configured to distribute collected information to a distributed ledger. Embodiments include a network-sensitive data collector that is configured to distribute collected information to a distributed ledger based on network conditions. Embodiments include a remotely organized data collector that is configured to distribute collected information to a distributed ledger based on intelligent, remote management of the distribution. Embodiments include a data collector with self-organizing local storage that is configured to distribute collected information to a distributed ledger. Embodiments include a system for data collection in an industrial environment using a distributed ledger for data storage and self-organizing network coding for data transport, wherein data storage is of a data structure supporting a haptic interface for data presentation, a heat map interface for data presentation, and/or an interface that operates with self-organized tuning of the interface layer.

[0396] As noted above, methods and systems are disclosed herein for a self-organizing collector, including a self-organizing, multi-sensor data collector that can optimize data collection, power and/or yield based on conditions in its environment, and is optionally responsive to remote organization. Embodiments include a self-organizing data collector that organizes at least in part based on network conditions. Embodiments include a self-organizing data collector with self-organizing storage for data collected in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with self-organizing data collection and self-organizing network coding for data transport. Embodiments include a system for data collection in an industrial environment with a self-organizing data collector that feeds a data structure supporting a haptic or multi-sensory wearable interface for data presentation, a heat map interface for data presentation, and/or an interface that operates with self-organized tuning of the interface layer.

[0397] As noted above, methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing, and/or other network conditions. Embodiments include a remotely organized, network condition-sensitive universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment, including network conditions. Embodiments include a network-condition sensitive data collector with self-organizing storage for data collected in an industrial data collection environment. Embodiments include a network-condition sensitive data collector with self-organizing network coding for data transport in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with a network-sensitive data collector that relays a data structure supporting a haptic wearable interface for data presentation, a heat map interface for data presentation, and/or an interface that operates with self-organized tuning of the interface layer.

[0398] As noted above, methods and systems are disclosed herein for a remotely organized universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment. Embodiments include a remotely organized universal data collector with self-organizing storage for data collected in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with remote control of data collection and self-organizing network coding for data transport. Embodiments include a remotely organized data collector for storing sensor data and delivering instructions for use of the data in a haptic or multi-sensory wearable interface, in a heat map visual interface, and/or in an interface that operates with self-organized tuning of the interface layer.

[0399] As noted above, methods and systems are disclosed herein for self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data. Embodiments include a system for data collection in an industrial environment with self-organizing data storage and self-organizing network coding for data transport. Embodiments include a data collector with self-organizing storage for storing sensor data and instructions for translating the data for use in a haptic wearable interface, in a heat map presentation interface, and/or in an interface that operates with self-organized tuning of the interface layer.

[0400] As noted above, methods and systems are disclosed herein for self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment. The system includes a data structure supporting a haptic wearable interface for data presentation, a heat map interface for data presentation, and/or self-organized tuning of an interface layer for data presentation.

[0401] As noted above, methods and systems are disclosed herein for a haptic or multi-sensory user interface, including a wearable haptic or multi-sensory user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. Embodiments include a wearable haptic user interface for conveying industrial state information from a data collector, with vibration, heat, electrical, and/or sound outputs. The wearable also has a visual presentation layer for presenting a heat map that indicates a parameter of the data. Embodiments include condition-sensitive, self-organized tuning of AR/VR interfaces and multi-sensory interfaces based on feedback metrics and/or training in industrial environments.

[0402] As noted above, methods and systems are disclosed herein for a presentation layer for AR/VR industrial glasses, where heat map elements are presented based on patterns and/or parameters in collected data. Embodiments include condition-sensitive, self-organized tuning of a heat map AR/VR interface based on feedback metrics and/or training in industrial environments. As noted above, methods and systems are disclosed herein for condition-sensitive, self-organized tuning of AR/VR interfaces based on feedback metrics and/or training in industrial environments.

[0403] The following illustrative clauses describe certain embodiments of the present disclosure. The data collection system mentioned in the following disclosure may be a local data collection system 102, a host processing system 112 (e.g., using a cloud platform), or a combination of a local system and a host system. In embodiments, a data collection system or data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and, in some embodiments, having IP front-end-end signal conditioning on a multiplexer for improved signal-to-noise ratio, multiplexer continuous monitoring alarming features, the use of distributed CPLD chips with a dedicated bus for logic control of multiple MUX and data acquisition sections, high-amperage input capability using solid state relays and design topology, power-down capability of at least one of an analog sensor channel and of a component board, unique electrostatic protection for trigger and vibration inputs, and/or precise voltage reference for A/D zero reference.

[0404] In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information, digital derivation of phase relative to input and trigger channels using on-board timers, a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection, the routing of a trigger channel that is either raw or buffered into other analog channels, the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements, and/or the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling.

[0405] In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having long blocks of data at a high-sampling rate, as opposed to multiple sets of data taken at different sampling rates, storage of calibration data with a maintenance history on-board card set, a rapid route creation capability using hierarchical templates, intelligent management of data collection bands, and/or a neural net expert system using intelligent management of data collection bands.

[0406] In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having use of a database hierarchy in sensor data analysis, an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system, a graphical approach for back-calculation definition, proposed bearing analysis methods, torsional vibration detection/analysis utilizing transitory signal analysis, and/or improved integration using both analog and digital methods.

[0407] In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment, data acquisition parking features, a self-sufficient data acquisition box, SD card storage, extended onboard statistical capabilities for continuous monitoring, the use of ambient, local and vibration noise for prediction, smart route changes based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation, smart ODS and transfer functions, a hierarchical multiplexer, identification of sensor overload, and/or RF identification and an inclinometer.

[0408] In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having continuous ultrasonic monitoring, cloud-based, machine pattern recognition based on the fusion of remote, analog industrial sensors, cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system, cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices, on-device sensor fusion and data storage for industrial IoT devices, a self-organizing data marketplace for industrial IoT data, self-organization of data pools based on utilization and/or yield metrics, training AI models based on industry-specific feedback, a self-organized swarm of industrial data collectors, an IoT distributed ledger, a self-organizing collector, a network-sensitive collector, a remotely organized collector, a self-organizing storage for a multi-sensor data collector, a self-organizing network coding for multi-sensor data network, a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs, heat maps displaying collected data for AR/VR, and/or automatically tuned AR/VR visualization of data collected by a data collector.

[0409] In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: multiplexer continuous monitoring alarming features; IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio; the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: high-amperage input capability using solid state relays and design topology; power-down capability of at least one analog sensor channel and of a component board; unique electrostatic protection for trigger and vibration inputs; precise voltage reference for A/D zero reference; and a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: digital derivation of phase relative to input and trigger channels using on-board timers; a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection; routing of a trigger channel that is either raw or buffered into other analog channels; the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements; and the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates; storage of calibration data with a maintenance history on-board card set; a rapid route creation capability using hierarchical templates; intelligent management of data collection bands; and a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: use of a database hierarchy in sensor data analysis; an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system; and a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: proposed bearing analysis methods; torsional vibration detection/analysis utilizing transitory signal; improved integration using both analog and digital methods; adaptive scheduling techniques for continuous monitoring of analog data in a local environment; data acquisition parking features; a self-sufficient data acquisition box; and SD card storage. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: extended onboard statistical capabilities for continuous monitoring; the use of ambient, local, and vibration noise for prediction; smart route changes based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation; smart ODS and transfer functions; and a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: identification of sensor overload; RF identification and an inclinometer; continuous ultrasonic monitoring; machine pattern recognition based on the fusion of remote, analog industrial sensors; and cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices; on-device sensor fusion and data storage for industrial IoT devices; a self-organizing data marketplace for industrial IoT data; and self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: training AI models based on industry-specific feedback; a self-organized swarm of industrial data collectors; an IoT distributed ledger; a self-organizing collector; and a network-sensitive collector. In embodiments, a data collection and processing system is provided having IP front-end signal conditioning on a multiplexer for improved signal-to-noise ratio and having at least one of: a remotely organized collector; a self-organizing storage for a multi-sensor data collector; a self-organizing network coding for multi-sensor data network; a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs; heat maps displaying collected data for AR/VR; and automatically tuned AR/VR visualization of data collected by a data collector.

[0410] In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections; high-amperage input capability using solid state relays and design topology; power-down capability of at least one of an analog sensor channel and/or of a component board; unique electrostatic protection for trigger and vibration inputs; and precise voltage reference for A/D zero reference. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information; digital derivation of phase relative to input and trigger channels using on-board timers; a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection; and routing of a trigger channel that is either raw or buffered into other analog channels. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements; the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling; long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates; storage of calibration data with a maintenance history on-board card set; and a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: intelligent management of data collection bands; a neural net expert system using intelligent management of data collection bands; use of a database hierarchy in sensor data analysis; and an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: a graphical approach for back-calculation definition; proposed bearing analysis methods; torsional vibration detection/analysis utilizing transitory signal analysis; and improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of adaptive scheduling techniques for continuous monitoring of analog data in a local environment; data acquisition parking features; a self-sufficient data acquisition box; and SD card storage. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: extended onboard statistical capabilities for continuous monitoring; the use of ambient, local and vibration noise for prediction; smart route changes based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation; and smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: a hierarchical multiplexer; identification of sensor overload; RF identification, and an inclinometer; cloud-based, machine pattern recognition based on the fusion of remote, analog industrial sensors; and machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices; on-device sensor fusion and data storage for industrial IoT devices; a self-organizing data marketplace for industrial IoT data; self-organization of data pools based on utilization and/or yield metrics; and training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: a self-organized swarm of industrial data collectors; an IoT distributed ledger; a self-organizing collector; a network-sensitive collector; and a remotely organized collector. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: a self-organizing storage for a multi-sensor data collector; and a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having multiplexer continuous monitoring alarming features and having at least one of: a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs; heat maps displaying collected data for AR/VR; and automatically tuned AR/VR visualization of data collected by a data collector.

[0411] In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having high-amperage input capability using solid state relays and design topology. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having power-down capability of at least one of an analog sensor channel and of a component board. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having unique electrostatic protection for trigger and vibration inputs. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having precise voltage reference for A/D zero reference. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having digital derivation of phase relative to input and trigger channels using on-board timers. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having routing of a trigger channel that is either raw or buffered into other analog channels. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having storage of calibration data with a maintenance history on-board card set. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having data acquisition parking features. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having SD card storage. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having smart route changes based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having identification of sensor overload. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a remotely organized collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having automatically tuned AR/VR visualization of data collected by a data collector.

[0412] In embodiments, a data collection and processing system is provided having one or more of high-amperage input capability using solid state relays and design topology, power-down capability of at least one of an analog sensor channel and of a component board, unique electrostatic protection for trigger and vibration inputs, precise voltage reference for A/D zero reference, a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information, digital derivation of phase relative to input and trigger channels using on-board timers, a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection, routing of a trigger channel that is either raw or buffered into other analog channels, the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize anti-aliasing (AA) filter requirements, the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling, long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates, storage of calibration data with a maintenance history on-board card set, a rapid route creation capability using hierarchical templates, intelligent management of data collection bands, a neural net expert system using intelligent management of data collection bands, use of a database hierarchy in sensor data analysis, an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system, a graphical approach for back-calculation definition, proposed bearing analysis methods, torsional vibration detection/analysis utilizing transitory signal analysis, improved integration using both analog and digital methods, adaptive scheduling techniques for continuous monitoring of analog data in a local environment, data acquisition parking features, a self-sufficient data acquisition box, SD card storage, extended onboard statistical capabilities for continuous monitoring, the use of ambient, local, and vibration noise for prediction, smart route changes based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation, smart ODS and transfer functions, a hierarchical multiplexer, identification of sensor overload, RF identification and an inclinometer, continuous ultrasonic monitoring, cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors, cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system, cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices, on-device sensor fusion and data storage for industrial IoT devices, a self-organizing data marketplace for industrial IoT data, self-organization of data pools based on utilization and/or yield metrics, training AI models based on industry-specific feedback, a self-organized swarm of industrial data collectors, an IoT distributed ledger, a self-organizing collector, a network-sensitive collector, a remotely organized collector, a self-organizing storage for a multi-sensor data collector, a self-organizing network coding for multi-sensor data network, a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs, heat maps displaying collected data for AR/VR, or automatically tuned AR/VR visualization of data collected by a data collector.

[0413] In embodiments, a platform is provided having one or more of cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors, cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system, a cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices, on-device sensor fusion and data storage for industrial IoT devices, a self-organizing data marketplace for industrial IoT data, self-organization of data pools based on utilization and/or yield metrics, training AI models based on industry-specific feedback, a self-organized swarm of industrial data collectors, an IoT distributed ledger, a self-organizing collector, a network-sensitive collector, a remotely organized collector, a self-organizing storage for a multi-sensor data collector, a self-organizing network coding for multi-sensor data network, a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs, heat maps displaying collected data for AR/VR, or automatically tuned AR/VR visualization of data collected by a data collector.

[0414] With regard to FIG. 18, a range of existing data sensing and processing systems with industrial sensing, processing, and storage systems 4500 include a streaming data collector 4510 that may be configured to accept data in a range of formats as described herein. In embodiments, the range of formats can include a data format A 4520, a data format B 4522, a data format C 4524, and a data format D 4528 that may be sourced from a range of sensors. Moreover, the range of sensors can include an instrument A 4540, an instrument B 4542, an instrument C 4544, and an instrument D 4548. The streaming data collector 4510 may be configured with processing capabilities that enable access to the individual formats while leveraging the streaming, routing, self-organizing storage, and other capabilities described herein.

[0415] FIG. 19 depicts methods and systems 4600 for industrial machine sensor data streaming collection, processing, and storage that facilitate use of a streaming data collector 4610 to collect and obtain data from legacy instruments 4620 and streaming instruments 4622. Legacy instruments 4620 and their data methodologies may capture and provide data that is limited in scope, due to the legacy systems and acquisition procedures, such as existing data methodologies described above herein, to a particular range of frequencies and the like. The streaming data collector 4610 may be configured to capture streaming instrument data 4632 as well as legacy instrument data 4630. The streaming data collector 4610 may also be configured to capture current streaming instruments 4622 and legacy instruments 4620 and sensors using current and legacy data methodologies. These embodiments may be useful in transition applications from the legacy instruments and processing to the streaming instruments and processing that may be current or desired instruments or methodologies. In embodiments, the streaming data collector 4610 may be configured to process the legacy instrument data 4630 so that it can be stored compatibly with the streamed instrument data 4632. The streaming data collector 4610 may process or parse the streamed instrument data 4632 based on the legacy instrument data 4630 to produce at least one extraction of the streamed data 4642 that is compatible with the legacy instrument data 4630 that can be processed into translated legacy data 4640. In embodiments, extracted data 4650 that can include extracted portions of translated legacy data 4652 and streamed data 4654 may be stored in a format that facilitates access and processing by legacy instrument data processing and further processing that can emulate legacy instrument data processing methods, and the like. In embodiments, the portions of the translated legacy data 4652 may also be stored in a format that facilitates processing with different methods that can take advantage of the greater frequencies, resolution, and volume of data possible with a streaming instrument.

[0416] FIG. 20 depicts alternate embodiments descriptive of methods and systems 4700 for industrial machine sensor data streaming, collection, processing, and storage that facilitate integration of legacy instruments and processing. In embodiments, a streaming data collector 4710 may be connected with an industrial machine 4712 and may include a plurality of sensors, such as streaming sensors 4720 and 4722 that may be configured to sense aspects of the industrial machine 4712 associated with at least one moving part of the machine 4712. The sensors 4720 and 4722 (or more) may communicate with one or more streaming devices 4740 that may facilitate streaming data from one or more of the sensors to the streaming data collector 4710. In embodiments, the industrial machine 4712 may also interface with or include one or more legacy instruments 4730 that may capture data associated with one or more moving parts of the industrial machine 4712 and store that data into a legacy data storage facility 4732.

[0417] In embodiments, a frequency and/or resolution detection facility 4742 may be configured to facilitate detecting information about legacy instrument sourced data, such as a frequency range of the data or a resolution of the data, and the like. The detection facility 4742 may operate on data directly from the legacy instruments 4730 or from data stored in a legacy storage facility 4732. The detection facility 4742 may communicate information detected about the legacy instruments 4730, its sourced data, and its stored data 4732, or the like to the streaming data collector 4710. Alternatively, the detection facility 4742 may access information, such as information about frequency ranges, resolution, and the like that characterizes the sourced data from the legacy instrument 4730 and/or may be accessed from a portion of the legacy storage facility 4732.

[0418] In embodiments, the streaming data collector 4710 may be configured with one or more automatic processors, algorithms, and/or other data methodologies to match up information captured by the one or more legacy instruments 4730 with a portion of data being provided by the one or more streaming devices 4740 from the one or more industrial machines 4712. Data from streaming devices 4740 may include a wider range of frequencies and resolutions than the sourced data of legacy instruments 4730 and, therefore, filtering and other such functions can be implemented to extract data from the streaming devices 4740 that corresponds to the sourced data of the legacy instruments 4730 in aspects such as frequency range, resolution, and the like. In embodiments, the configured streaming data collector 4710 may produce a plurality of streams of data, including a stream of data that may correspond to the stream of data from the streaming device 4740 and a separate stream of data that is compatible, in some aspects, with the legacy instrument sourced data and the infrastructure to ingest and automatically process it. Alternatively, the streaming data collector 4710 may output data in modes other than as a stream, such as batches, aggregations, summaries, and the like.

[0419] Configured streaming data collector 4710 may communicate with a stream storage facility 4764 for storing at least one of the data outputs from the streaming device 4710 and data extracted therefrom that may be compatible, in some aspects, with the sourced data of the legacy instruments 4730. A legacy compatible output of the configured streaming data collector 4710 may also be provided to a format adaptor facility 4748, 4760 that may configure, adapt, reformat, and make other adjustments to the legacy compatible data so that it can be stored in a legacy compatible storage facility 4762 so that legacy processing facilities 4744 may execute data processing methods on data in the legacy compatible storage facility 4762 and the like that are configured to process the sourced data of the legacy instruments 4730. In embodiments in which legacy compatible data is stored in the stream storage facility 4764, legacy processing facility 4744 may also automatically process this data after optionally being processed by format adaptor 4760. By arranging the data collection, streaming, processing, formatting, and storage elements to provide data in a format that is fully compatible with legacy instrument sourced data, transition from a legacy system can be simplified, and the sourced data from legacy instruments can be easily compared to newly acquired data (with more content) without losing the legacy value of the sourced data from the legacy instruments 4730.

[0420] FIG. 21 depicts alternate embodiments of the methods and systems 4800 described herein for industrial machine sensor data streaming, collection, processing, and storage that may be compatible with legacy instrument data collection and processing. In embodiments, processing industrial machine sensed data may be accomplished in a variety of ways including aligning legacy and streaming sources of data, such as by aligning stored legacy and streaming data; aligning stored legacy data with a stream of sensed data; and aligning legacy and streamed data as it is being collected. In embodiments, an industrial machine 4810 may include, communicate with, or be integrated with one or more stream data sensors 4820 that may sense aspects of the industrial machine 4810 such as aspects of one or more moving parts of the machine. The industrial machine 4810 may also communicate with, include, or be integrated with one or more legacy data sensors 4830 that may sense similar aspects of the industrial machine 4810. In embodiments, the one or more legacy data sensors 4830 may provide sensed data to one or more legacy data collectors 4840. The stream data sensors 4820 may produce an output that encompasses all aspects of (i.e., a richer signal) and is compatible with sensed data from the legacy data sensors 4830. The stream data sensors 4820 may provide compatible data to the legacy data collector 4840. By mimicking the legacy data sensors 4830 or their data streams, the stream data sensors 4820 may replace (or serve as suitable duplicate for) one or more legacy data sensors, such as during an upgrade of the sensing and processing system of an industrial machine. Frequency range, resolution, and the like may be mimicked by the stream data so as to ensure that all forms of legacy data are captured or can be derived from the stream data. In embodiments, format conversion, if needed, can also be performed by the stream data sensors 4820. The stream data sensors 4820 may also produce an alternate data stream that is suitable for collection by the stream data collector 4850. In embodiments, such an alternate data stream may be a superset of the legacy data sensor data in at least one or more of: frequency range, resolution, duration of sensing the data, and the like.

[0421] In embodiments, an industrial machine sensed data processing facility 4860 may execute a wide range of sensed data processing methods, some of which may be compatible with the data from legacy data sensors 4830 and may produce outputs that may meet legacy sensed data processing requirements. To facilitate use of a wide range of data processing capabilities of processing facility 4860, legacy and stream data may need to be aligned so that a compatible portion of stream data may be extracted for processing with legacy compatible methods and the like. In embodiments, FIG. 21 depicts three different techniques for aligning stream data to legacy data. A first alignment methodology 4862 includes aligning legacy data output by the legacy data collector 4840 with stream data output by the stream data collector 4850. As data is provided by the legacy data collector 4840, aspects of the data may be detected, such as resolution, frequency, duration, and the like, and may be used as control for a processing method that identifies portions of a stream of data from the stream data collector 4850 that are purposely compatible with the legacy data. The processing facility 4860 may apply one or more legacy compatible methods on the identified portions of the stream data to extract data that can be easily compared to or referenced against the legacy data.

[0422] In embodiments, a second alignment methodology 4864 may involve aligning streaming data with data from a legacy storage facility 4882. In embodiments, a third alignment methodology 4868 may involve aligning stored stream data from a stream storage facility 4884 with legacy data from the legacy data storage facility 4882. In each of the methodologies 4862, 4864, 4868, alignment data may be determined by processing the legacy data to detect aspects such as resolution, duration, frequency range, and the like. Alternatively, alignment may be performed by an alignment facility, such as facilities using methodologies 4862, 4864, 4868 that may receive or may be configured with legacy data descriptive information such as legacy frequency range, duration, resolution, and the like.

[0423] In embodiments, an industrial machine sensing data processing facility 4860 may have access to legacy compatible methods and algorithms that may be stored in a legacy data methodology storage facility 4880. These methodologies, algorithms, or other data in the legacy algorithm storage facility 4880 may also be a source of alignment information that could be communicated by the industrial machine sensed data processing facility 4860 to the various alignment facilities having methodologies 4862, 4864, 4868. By having access to legacy compatible algorithms and methodologies, the data processing facility 4860 may facilitate processing legacy data, streamed data that is compatible with legacy data, or portions of streamed data that represent the legacy data to produce legacy compatible analytics.

[0424] In embodiments, the data processing facility 4860 may execute a wide range of other sensed data processing methods, such as wavelet derivations and the like, to produce streamed data analytics 4892. In embodiments, the streaming data collector 102, 4510, 4610, 4710 (FIGS. 3, 6, 18, 19, 20) or data processing facility 4860 may include portable algorithms, methodologies, and inputs that may be defined and extracted from data streams. In many examples, a user or enterprise may already have existing and effective methods related to analyzing specific pieces of machinery and assets. These existing methods could be imported into the configured streaming data collector 102, 4510, 4610, 4710 or the data processing facility 4860 as portable algorithms or methodologies. Data processing, such as described herein for the configured streaming data collector 102, 4510, 4610, 4710 may also match an algorithm or methodology to a situation, then extract data from a stream to match to the data methodology from the legacy acquisition or legacy acquisition techniques. In embodiments, the streaming data collector 102, 4510, 4610, 4710 may be compatible with many types of systems and may be compatible with systems having varying degrees of criticality.

[0425] Exemplary industrial machine deployments of the methods and systems described herein are now described. An industrial machine may be a gas compressor. In an example, a gas compressor may operate an oil pump on a very large turbo machine, such as a very large turbo machine that includes 10,000 HP motors. The oil pump may be a highly critical system as its failure could cause an entire plant to shut down. The gas compressor in this example may run four stages at a very high frequency, such as 36,000 RPM, and may include tilt pad bearings that ride on an oil film. The oil pump in this example may have roller bearings, such that if an anticipated failure is not being picked up by a user, the oil pump may stop running, and the entire turbo machine would fail. Continuing with this example, the streaming data collector 102, 4510, 4610, 4710 may collect data related to vibrations, such as casing vibration and proximity probe vibration. Other bearings industrial machine examples may include generators, power plants, boiler feed pumps, fans, forced draft fans, induced draft fans, and the like. The streaming data collector 102, 4510, 4610, 4710 for a bearings system used in the industrial gas industry may support predictive analysis on the motors, such as that performed by model-based expert systems—for example, using voltage, current, and vibration as analysis metrics.

[0426] Another exemplary industrial machine deployment may be a motor and the streaming data collector 102, 4510, 4610, 4710 that may assist in the analysis of a motor by collecting voltage and current data on the motor, for example.

[0427] Yet another exemplary industrial machine deployment may include oil quality sensing. An industrial machine may conduct oil analysis, and the streaming data collector 102, 4510, 4610, 4710 may assist in searching for fragments of metal in oil, for example.

[0428] The methods and systems described herein may also be used in combination with model-based systems. Model-based systems may integrate with proximity probes. Proximity probes may be used to sense problems with machinery and shut machinery down due to sensed problems. A model-based system integrated with proximity probes may measure a peak waveform and send a signal that shuts down machinery based on the peak waveform measurement.

[0429] Enterprises that operate industrial machines may operate in many diverse industries. These industries may include industries that operate manufacturing lines, provide computing infrastructure, support financial services, provide HVAC equipment, and the like. These industries may be highly sensitive to lost operating time and the cost incurred due to lost operating time. HVAC equipment enterprises in particular may be concerned with data related to ultrasound, vibration, IR, and the like, and may get much more information about machine performance related to these metrics using the methods and systems of industrial machine sensed data streaming collection than from legacy systems.

[0430] Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for capturing a plurality of streams of sensed data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine; at least one of the streams containing a plurality of frequencies of data. The method may include identifying a subset of data in at least one of the multiple streams that corresponds to data representing at least one predefined frequency. The at least one predefined frequency is represented by a set of data collected from alternate sensors deployed to monitor aspects of the industrial machine associated with the at least one moving part of the machine. The method may further include processing the identified data with a data processing facility that processes the identified data with data methodologies configured to be applied to the set of data collected from alternate sensors. Lastly, the method may include storing the at least one of the streams of data, the identified subset of data, and a result of processing the identified data in an electronic data set.

[0431] The methods and systems may include a method for applying data captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the data captured with predefined lines of resolution covering a predefined frequency range, to a frequency matching facility that identifies a subset of data streamed from other sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine, the streamed data comprising a plurality of lines of resolution and frequency ranges, the subset of data identified corresponding to the lines of resolution and predefined frequency range. This method may include storing the subset of data in an electronic data record in a format that corresponds to a format of the data captured with predefined lines of resolution, and signaling to a data processing facility the presence of the stored subset of data. This method may optionally include processing the subset of data with at least one of algorithms, methodologies, models, and pattern recognizers that corresponds to algorithms, methodologies, models, and pattern recognizers associated with processing the data captured with predefined lines of resolution covering a predefined frequency range.

[0432] The methods and systems may include a method for identifying a subset of streamed sensor data. The sensor data is captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. The subset of streamed sensor data is at predefined lines of resolution for a predefined frequency range. The method includes establishing a first logical route for communicating electronically between a first computing facility performing the identifying and a second computing facility. The identified subset of the streamed sensor data is communicated exclusively over the established first logical route when communicating the subset of streamed sensor data from the first facility to the second facility. This method may further include establishing a second logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that is not the identified subset. This method may further include establishing a third logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that includes the identified subset and at least one other portion of the data not represented by the identified subset.

[0433] The methods and systems may include a first data sensing and processing system that captures first data from a first set of sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the first data covering a set of lines of resolution and a frequency range. This system may include a second data sensing and processing system that captures and streams a second set of data from a second set of sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine, the second data covering a plurality of lines of resolution that includes the set of lines of resolution and a plurality of frequencies that includes the frequency range. The system may enable: (1) selecting a portion of the second data that corresponds to the set of lines of resolution and the frequency range of the first data; and (2) processing the selected portion of the second data with the first data sensing and processing system.

[0434] The methods and systems may include a method for automatically processing a portion of a stream of sensed data. The sensed data received from a first set of sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine in response to an electronic data structure that facilitates extracting a subset of the stream of sensed data that corresponds to a set of sensed data received from a second set of sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The set of sensed data is constrained to a frequency range. The stream of sensed data includes a range of frequencies that exceeds the frequency range of the set of sensed data. The processing comprises executing data methodologies on a portion of the stream of sensed data that is constrained to the frequency range of the set of sensed data. The data methodologies are configured to process the set of sensed data.

[0435] The methods and systems may include a method for receiving first data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. This method may further include: (1) detecting at least one of a frequency range and lines of resolution represented by the first data, and (2) receiving a stream of data from sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The stream of data includes: a plurality of frequency ranges and a plurality of lines of resolution that exceeds the frequency range and the lines of resolution represented by the first data; extracting a set of data from the stream of data that corresponds to at least one of the frequency range and the lines of resolution represented by the first data; and processing the extracted set of data with a data processing method that is configured to process data within the frequency range and within the lines of resolution of the first data.

[0436] The methods and systems disclosed herein may include, connect to, or be integrated with a data acquisition instrument and in the many embodiments, FIG. 22 shows methods and systems 5000 that includes a data acquisition (DAQ) streaming instrument 5002 also known as an SDAQ. In embodiments, output from sensors 5010, 5012, 5014 may be of various types including vibration, temperature, pressure, ultrasound and so on. In my many examples, one of the sensors may be used. In further examples, many of the sensors may be used and their signals may be used individually or in predetermined combinations and/or at predetermined intervals, circumstances, setups, and the like.

[0437] In embodiments, the output signals from the sensors 5010, 5012, 5014 may be fed into instrument inputs 5020, 5022, 5024 of the DAQ instrument 5002 and may be configured with additional streaming capabilities 5028. By way of these many examples, the output signals from the sensors 5010, 5012, 5014, or more as applicable, may be conditioned as an analog signal before digitization with respect to at least scaling and filtering. The signals may then be digitized by an analog-to-digital converter 5030. The signals received from all relevant channels (i.e., one or more channels are switched on manually, by alarm, by route, and the like) may be simultaneously sampled at a predetermined rate sufficient to perform the maximum desired frequency analysis that may be adjusted and readjusted as needed or otherwise held constant to ensure compatibility or conformance with other relevant datasets. In embodiments, the signals are sampled for a relatively long time and gap-free as one continuous stream so as to enable further post-processing at lower sampling rates with sufficient individual sampling.

[0438] In embodiments, data may be streamed from a collection of points and then the next set of data may be collected from additional points according to a prescribed sequence, route, path, or the like. In many examples, the sensors 5010, 5012, 5014 or more may be moved to the next location according to the prescribed sequence, route, pre-arranged configurations, or the like. In certain examples, not all of the sensors 5010, 5012, 5014 may move and therefore some may remain fixed in place and used for detection of reference phase or the like.

[0439] In embodiments, a multiplex (mux) 5032 may be used to switch to the next collection of points, to a mixture of the two methods or collection patterns that may be combined, other predetermined routes, and the like. The multiplexer 5032 may be stackable so as to be laddered and effectively accept more channels than the DAQ instrument 5002 provides. In examples, the DAQ instrument 5002 may provide eight channels while the multiplexer 5032 may be stacked to supply 32 channels. Further variations are possible with one more multiplexers. In embodiments, the multiplexer 5032 may be fed into the DAQ instrument 5002 through an instrument input 5024. In embodiments, the DAQ instrument 5002 may include a controller 5038 that may take the form of an onboard controller, a PC, other connected devices, network based services, and combinations thereof.

[0440] In embodiments, the sequence and panel conditions used to govern the data collection process may be obtained from the multimedia probe (MMP) and probe control, sequence and analytical (PCSA) information store 5040. In embodiments, the information store 5040 may be onboard the DAQ instrument 5002. In embodiments, contents of the information store 5040 may be obtained through a cloud network facility, from other DAQ instruments, from other connected devices, from the machine being sensed, other relevant sources, and combinations thereof. In embodiments, the information store 5040 may include such items as the hierarchical structural relationships of the machine, e.g., a machine contains predetermined pieces of equipment, each of which may contain one or more shafts and each of those shafts may have multiple associated bearings. Each of those types of bearings may be monitored by specific types of transducers or probes, according to one or more specific prescribed sequences (paths, routes, and the like) and with one or more specific panel conditions that may be set on the one or more DAQ instruments 5002. By way of this example, the panel conditions may include hardware specific switch settings or other collection parameters. In many examples, collection parameters include but are not limited to a sampling rate, AC/DC coupling, voltage range and gain, integration, high and low pass filtering, anti-aliasing filtering, ICP™ transducers and other integrated-circuit piezoelectric transducers, 4-20 mA loop sensors, and the like. In embodiments, the information store 5040 may also include machinery specific features that may be important for proper analysis such as gear teeth for a gear, number blades in a pump impeller, number of motor rotor bars, bearing specific parameters necessary for calculating bearing frequencies, revolution per minutes information of all rotating elements and multiples of those RPM ranges, and the like. Information in the information store may also be used to extract stream data 5050 for permanent storage.

[0441] Based on directions from the DAQ API software 5052, digitized waveforms may be uploaded using DAQ driver services 5054 of a driver onboard the DAQ instrument 5002. In embodiments, data may then be fed into a raw data server 5058 which may store the stream data 5050 in a stream data repository 5060. In embodiments, this data storage area is typically meant for storage until the data is copied off of the DAQ instrument 5002 and verified. The DAQ API 5052 may also direct the local data control application 5062 to extract and process the recently obtained stream data 5050 and convert it to the same or lower sampling rates of sufficient length to affect one or more desired resolutions. By way of these examples, this data may be converted to spectra, averaged, and processed in a variety of ways and stored, at least temporarily, as extracted/processed (EP) data 5064. It will be appreciated in light of the disclosure that legacy data may require its own sampling rates and resolution to ensure compatibility and often this sampling rate may not be integer proportional to the acquired sampling rate. It will also be appreciated in light of the disclosure that this may be especially relevant for order-sampled data whose sampling frequency is related directly to an external frequency (typically the running speed of the machine or its local componentry) rather than the more-standard sampling rates employed by the internal crystals, clock functions, or the like of the DAQ instrument (e.g., values of Fmax of 100, 200, 500, 1K, 2K, 5K, 10K, 20K, and so on).

[0442] In embodiments, the extract/process (EP) align module 5068 of the local data control application 5062 may be able to fractionally adjust the sampling rates to these non-integer ratio rates satisfying an important requirement for making data compatible with legacy systems. In embodiments, fractional rates may also be converted to integer ratio rates more readily because the length of the data to be processed may be adjustable. It will be appreciated in light of the disclosure that if the data was not streamed and just stored as spectra with the standard or predetermined Fmax, it may be impossible in certain situations to convert it retroactively and accurately to the order-sampled data. It will also be appreciated in light of the disclosure that internal identification issues may also need to be reconciled. In many examples, stream data may be converted to the proper sampling rate and resolution as described and stored (albeit temporarily) in an EP legacy data repository 5070 to ensure compatibility with legacy data.

[0443] To support legacy data identification issues, a user input module 5072 is shown in many embodiments should there be no automated process (whether partially or wholly) for identification translation. In such examples, one or more legacy systems (i.e., pre-existing data acquisition) may be characterized in that the data to be imported is in a fully standardized format such as a Mimosa™ format, and other similar formats. Moreover, sufficient indentation of the legacy data and/or the one or more machines from which the legacy data was produced may be required in the completion of an identification mapping table 5074 to associate and link a portion of the legacy data to a portion of the newly acquired streamed data 5050. In many examples, the end user and/or legacy vendor may be able to supply sufficient information to complete at least a portion of a functioning identification (ID) mapping table 5074 and therefore may provide the necessary database schema for the raw data of the legacy system to be used for comparison, analysis, and manipulation of newly streamed data 5050.

[0444] In embodiments, the local data control application 5062 may also direct streaming data as well as extracted/processed (EP) data to a cloud network facility 5080 via wired or wireless transmission. From the cloud network facility 5080 other devices may access, receive, and maintain data including the data from a master raw data server (MRDS) 5082. The movement, distribution, storage, and retrieval of data remote to the DAQ instrument 5002 may be coordinated by the cloud data management services (“CDMS”) 5084.

[0445] FIG. 23 shows additional methods and systems that include the DAQ instrument 5002 accessing related cloud-based services. In embodiments, the DAQ API 5052 may control the data collection process as well as its sequence. By way of these examples, the DAQ API 5052 may provide the capability for editing processes, viewing plots of the data, controlling the processing of that data, viewing the output data in all its myriad forms, analyzing this data including expert analysis, and communicating with external devices via the local data control application 5062 and with the CDMS 5084 via the cloud network facility 5080. In embodiments, the DAQ API 5052 may also govern the movement of data, its filtering, as well as many other housekeeping functions.

[0446] In embodiments, an expert analysis module 5100 may generate reports 5102 that may use machine or measurement point specific information from the information store 5040 to analyze the stream data 5050 using a stream data analyzer module 5104 and the local data control application 5062 with the extract/process (“EP”) align module 5068. In embodiments, the expert analysis module 5100 may generate new alarms or ingest alarm settings into an alarms module 5108 that is relevant to the stream data 5050. In embodiments, the stream data analyzer module 5104 may provide a manual or automated mechanism for extracting meaningful information from the stream data 5050 in a variety of plotting and report formats. In embodiments, a supervisory control of the expert analysis module 5100 is provided by the DAQ API 5052. In further examples, the expert analysis module 5100 may be supplied (wholly or partially) via the cloud network facility 5080. In many examples, the expert analysis module 5100 via the cloud may be used rather than a locally-deployed expert analysis module 5100 for various reasons such as using the most up-to-date software version, more processing capability, a bigger volume of historical data to reference, and so on. In many examples, it may be important that the expert analysis module 5100 be available when an internet connection cannot be established so having this redundancy may be crucial for seamless and time efficient operation. Toward that end, many of the modular software applications and databases available to the DAQ instrument 5002 where applicable may be implemented with system component redundancy to provide operational robustness to provide connectivity to cloud services when needed but also operate successfully in isolated scenarios where connectivity is not available and sometime not available purposefully to increase security and the like.

[0447] In embodiments, the DAQ instrument acquisition may require a real time operating system (“RTOS”) for the hardware especially for streamed gap-free data that is acquired by a PC. In some instances, the requirement for a RTOS may result in (or may require) expensive custom hardware and software capable of running such a system. In many embodiments, such expensive custom hardware and software may be avoided and an RTOS may be effectively and sufficiently implemented using a standard Windows™ operating systems or similar environments including the system interrupts in the procedural flow of a dedicated application included in such operating systems.

[0448] The methods and systems disclosed herein may include, connect to, or be integrated with one or more DAQ instruments and in the many embodiments, FIG. 24 shows methods and systems 5150 that include the DAQ instrument 5002 (also known as a streaming DAQ or an SDAQ). In embodiments, the DAQ instrument 5002 may effectively and sufficiently implement an RTOS using standard windows operating system (or other similar personal computing systems) that may include a software driver configured with a First In, First Out (FIFO) memory area 5152. The FIFO memory area 5152 may be maintained and hold information for a sufficient amount of time to handle a worst-case interrupt that it may face from the local operating system to effectively provide the RTOS. In many examples, configurations on a local personal computer or connected device may be maintained to minimize operating system interrupts. To support this, the configurations may be maintained, controlled, or adjusted to eliminate (or be isolated from) any exposure to extreme environments where operating system interrupts may become an issue. In embodiments, the DAQ instrument 5002 may produce a notification, alarm, message, or the like to notify a user when any gap errors are detected. In these many examples, such errors may be shown to be rare and even if they occur, the data may be adjusted knowing when they occurred should such a situation arise.

[0449] In embodiments, the DAQ instrument 5002 may maintain a sufficiently large FIFO memory area 5152 that may buffer the incoming data so as to be not affected by operating system interrupts when acquiring data. It will be appreciated in light of the disclosure that the predetermined size of the FIFO memory area 5152 may be based on operating system interrupts that may include Windows system and application functions such as the writing of data to Disk or SSD, plotting, GUI interactions and standard Windows tasks, low-level driver tasks such as servicing the DAQ hardware and retrieving the data in bursts, and the like.

[0450] In embodiments, the computer, controller, connected device or the like that may be included in the DAQ instrument 5002 may be configured to acquire data from the one or more hardware devices over a USB port, firewire, ethernet, or the like. In embodiments, the DAQ driver services 5054 may be configured to have data delivered to it periodically so as to facilitate providing a channel specific FIFO memory buffer that may be configured to not miss data, i.e., it is gap-free. In embodiments, the DAQ driver services 5054 may be configured so as to maintain an even larger (than the device) channel specific FIFO area 5152 that it fills with new data obtained from the device. In embodiments, the DAQ driver services 5054 may be configured to employ a further process in that the raw data server 5058 may take data from the FIFO 5152 and may write it as a contiguous stream to non-volatile storage areas such as the stream data repository 5060 that may be configured as one or more disk drives, SSDs, or the like. In embodiments, the FIFO 5152 may be configured to include a starting and stopping marker or pointer to mark where the latest most current stream was written. By way of these examples, a FIFO end marker 5154 may be configured to mark the end of the most current data until it reaches the end of the spooler and then wraps around constantly cycling around. In these examples, there is always one megabyte (or other configured capacities) of the most current data available in the FIFO 5152 once the spooler fills up. It will be appreciated in light of the disclosure that further configurations of the FIFO memory area may be employed. In embodiments, the DAQ driver services 5054 may be configured to use the DAQ API 5052 to pipe the most recent data to a high-level application for processing, graphing and analysis purposes. In some examples, it is not required that this data be gap-free but even in these instances, it is helpful to identify and mark the gaps in the data. Moreover, these data updates may be configured to be frequent enough so that the user would perceive the data as live. In the many embodiments, the raw data is flushed to non-volatile storage without a gap at least for the prescribed amount of time and examples of the prescribed amount of time may be about thirty seconds to over four hours. It will be appreciated in light of the disclosure that many pieces of equipment and their components may contribute to the relative needed duration of the stream of gap-free data and those durations may be over four hours when relatively low speeds are present in large numbers, when non-periodic transient activity is occurring on a relatively long time frame, when duty cycle only permits operation in relevant ranges for restricted durations and the like.

[0451] With reference to FIG. 23, the stream data analyzer module 5104 may provide for the manual or extraction of information from the data stream in a variety of plotting and report formats. In embodiments, resampling, filtering (including anti-aliasing), transfer functions, spectrum analysis, enveloping, averaging, peak detection functionality, as well as a host of other signal processing tools, may be available for the analyst to analyze the stream data and to generate a very large array of snapshots. It will be appreciated in light of the disclosure that much larger arrays of snapshots are created than ever would have been possible by scheduling the collection of snapshots beforehand, i.e., during the initial data acquisition for the measurement point in question.

[0452] FIG. 25 depicts a display 5200 whose viewable content 5202 may be accessed locally or remotely, wholly or partially. In many embodiments, the display 5200 may be part of the DAQ instrument 5002, may be part of the PC or connected device 5038 that may be part of the DAQ instrument 5002, or its viewable content 5202 may be viewable from associated network connected displays. In further examples, the viewable content 5202 of the display 5200 or portions thereof may be ported to one or more relevant network addresses. In the many embodiments, the viewable content 5202 may include a screen 5204 that shows, for example, an approximately two-minute data stream 5208 may be collected at a sampling rate of 25.6 kHz for four channels 5220, 5222, 5224, 5228, simultaneously. By way of these examples and in these configurations, the length of the data may be approximately 3.1 megabytes. It will be appreciated in light of the disclosure that the data stream (including each of its four channels or as many as applicable) may be replayed in some aspects like a magnetic tape recording (e.g. a reel-to-reel or a cassette) with all of the controls normally associated with playback such as forward 5230, fast forward, backward 5232, fast rewind, step back, step forward, advance to time point, retreat to time point, beginning 5234, end, 5238, play 5240, stop 5242, and the like. Additionally, the playback of the data stream may further be configured to set a width of the data stream to be shown as a contiguous subset of the entire stream. In the example with a two-minute data stream, the entire two minutes may be selected by the “select all” button 5244, or some subset thereof may be selected with the controls on the screen 5204 or that may be placed on the screen 5204 by configuring the display 5200 and the DAQ instrument 5002. In this example, the “process selected data” button 5250 on the screen 5204 may be selected to commit to a selection of the data stream.

[0453] FIG. 26 depicts the many embodiments that include a screen 5250 on the display 5200 that shows results of selecting all of the data for this example. In embodiments, the screen 5250 in FIG. 26 may provide the same or similar playback capabilities as what is depicted on the screen 5204 shown in FIG. 25 but also includes resampling capabilities, waveform displays, and spectrum displays. In light of the disclosure, it will be appreciated that this functionality may permit the user to choose in many situations any Fmax less than that supported by the original streaming sampling rate. In embodiments, any section of any size may be selected and further processing, analytics, and tools for viewing and dissecting the data may be provided. In embodiments, the screen 5250 may include four windows 5252, 5254, 5258, 5260 that show the stream data from the four channels 5220, 5222, 5224, 5228 of FIG. 25. In embodiments, the screen 5250 may also include offset and overlap controls 5262, resampling controls 5264, and other similar controls.

[0454] In many examples, any one of many transfer functions may be established between any two channels, such as the two channels 5280, 5282 that may be shown on a screen 5284, shown on the display 5200, as depicted in FIG. 27. The selection of the two channels 5280, 5282 on the screen 5284 may permit the user to depict the output of the transfer function on any of the screens including screen 5284 and screen 5204.

[0455] In embodiments, FIG. 28 shows a high-resolution spectrum screen 5300 on the display 5200 with a waveform view 5302, full cursor control 5304 and a peak extraction view 5308. In these examples, the peak extraction view 5308 may be configured with a resolved configuration 5310 that may be configured to provide enhanced amplitude and frequency accuracy and may use spectral sideband energy distribution. The peak extraction view 5308 may also be configured with averaging 5312, phase and cursor vector information 5314, and the like.

[0456] In embodiments, FIG. 29 shows an enveloping screen 5350 on the display 5200 with a waveform view 5352, and a spectral format view 5354. The views 5352, 5354 on the enveloping screen 5350 may display modulation from the signal in both waveform and spectral formats. In embodiments, FIG. 30 shows a relative phase screen 5380 on the display 5200 with four phase views 5382, 5384, 5388, 5390. The four phase views 5382, 5384, 5388, 5390 relate to the on spectrum the enveloping screen 5350 that may display modulation from the signal in waveform format in view 5352 and spectral format in view 5354. In embodiments, the reference channel control 5392 may be selected to use channel four as a reference channel to determine relative phase between each of the channels.

[0457] It will be appreciated in light of the disclosure that the sampling rates of vibration data of up to 100 kHz (or higher in some scenarios) may be utilized for non-vibration sensors as well. In doing so, it will further be appreciated in light of the disclosure that stream data in such durations at these sampling rates may uncover new patterns to be analyzed due in no small part that many of these types of sensors have not been utilized in this manner. It will also be appreciated in light of the disclosure that different sensors used in machinery condition monitoring may provide measurements more akin to static levels rather than fast-acting dynamic signals. In some cases, faster response time transducers may have to be used prior to achieving the faster sampling rates.

[0458] In many embodiments, sensors may have a relatively static output such as temperature, pressure, or flow but may still be analyzed with the dynamic signal processing system and methodologies as disclosed herein. It will be appreciated in light of the disclosure that the time scale, in many examples, may be slowed down. In many examples, a collection of temperature readings collected approximately every minute for over two weeks may be analyzed for their variation solely or in collaboration or in fusion with other relevant sensors. By way of these examples, the direct current level or average level may be omitted from all the readings (e.g., by subtraction) and the resulting delta measurements may be processed (e.g., through a Fourier transform). From these examples, resulting spectral lines may correlate to specific machinery behavior or other symptoms present in industrial system processes. In further examples, other techniques include enveloping that may look for modulation, wavelets that may look for spectral patterns that last only for a short time (e.g., bursts), cross-channel analysis to look for correlations with other sensors including vibration, and the like.

[0459] FIG. 31 shows a DAQ instrument 5400 that may be integrated with one or more analog sensors 5402 and endpoint nodes 5404 to provide a streaming sensor 5410 or smart sensors that may take in analog signals and then process and digitize them, and then transmit them to one or more external monitoring systems 5412 in the many embodiments that may be connected to, interfacing with, or integrated with the methods and systems disclosed herein. The monitoring system 5412 may include a streaming hub server 5420 that may communicate with the CDMS 5084. In embodiments, the CDMS 5084 may contact, use, and integrate with cloud data 5430 and cloud services 5432 that may be accessible through one or more cloud network facilities 5080. In embodiments, the streaming hub server 5420 may connect with another streaming sensor 5440 that may include a DAQ instrument 5442, an endpoint node 5444, and the one or more analog sensors such as analog sensor 5448. The streaming hub server 5420 may connect with other streaming sensors such as the streaming sensor 5460 that may include a DAQ instrument 5462, an endpoint node 5464, and the one or more analog sensors such as analog sensor 5468.

[0460] In embodiments, there may be additional streaming hub servers such as the streaming hub server 5480 that may connect with other streaming sensors such as the streaming sensor 5490 that may include a DAQ instrument 5492, an endpoint node 5494, and the one or more analog sensors such as analog sensor 5498. In embodiments, the streaming hub server 5480 may also connect with other streaming sensors such as the streaming sensor 5500 that may include a DAQ instrument 5502, an endpoint node 5504, and the one or more analog sensors such as analog sensor 5508. In embodiments, the transmission may include averaged overall levels and in other examples may include dynamic signal sampled at a prescribed and/or fixed rate. In embodiments, the streaming sensors 5410, 5440, 5460, 5490, and 5500 may be configured to acquire analog signals and then apply signal conditioning to those analog signals including coupling, averaging, integrating, differentiating, scaling, filtering of various kinds, and the like. The streaming sensors 5410, 5440, 5460, 5490, and 5500 may be configured to digitize the analog signals at an acceptable rate and resolution (number of bits) and to process further the digitized signal when required. The streaming sensors 5410, 5440, 5460, 5490, and 5500 may be configured to transmit the digitized signals at pre-determined, adjustable, and re-adjustable rates. In embodiments, the streaming sensors 5410, 5440, 5460, 5490, and 5500 are configured to acquire, digitize, process, and transmit data at a sufficient effective rate so that a relatively consistent stream of data may be maintained for a suitable amount of time so that a large number of effective analyses may be shown to be possible. In the many embodiments, there would be no gaps in the data stream and the length of data should be relatively long, ideally for an unlimited amount of time, although practical considerations typically require ending the stream. It will be appreciated in light of the disclosure that this long duration data stream with effectively no gap in the stream is in contrast to the more commonly used burst collection where data is collected for a relatively short period of time (i.e., a short burst of collection), followed by a pause, and then perhaps another burst collection and so on. In the commonly used collections of data collected over noncontiguous bursts, data would be collected at a slow rate for low frequency analysis and high frequency for high frequency analysis. In many embodiments of the present disclosure, in contrast, the streaming data is being collected (i) once, (ii) at the highest useful and possible sampling rate, and (iii) for a long enough time that low frequency analysis may be performed as well as high frequency. To facilitate the collection of the streaming data, enough storage memory must be available on the one or more streaming sensors such as the streaming sensors 5410, 5440, 5460, 5490, 5500 so that new data may be off-loaded externally to another system before the memory overflows. In embodiments, data in this memory would be stored into and accessed from “First-In, First-Out” (“FIFO”) mode. In these examples, the memory with a FIFO area may be a dual port so that the sensor controller may write to one part of it while the external system reads from a different part. In embodiments, data flow traffic may be managed by semaphore logic.

[0461] It will be appreciated in light of the disclosure that vibration transducers that are larger in mass will have a lower linear frequency response range because the natural resonance of the probe is inversely related to the square root of the mass and will be lowered. Toward that end, a resonant response is inherently non-linear and so a transducer with a lower natural frequency will have a narrower linear passband frequency response. It will also be appreciated in light of the disclosure that above the natural frequency the amplitude response of the sensor will taper off to negligible levels rendering it even more unusable. With that in mind, high frequency accelerometers, for this reason, tend to be quite small in mass, to the order of half of a gram. It will also be appreciated in light of the disclosure that adding the required signal processing and digitizing electronics required for streaming may, in certain situations, render the sensors incapable in many instances of measuring high-frequency activity.

[0462] In embodiments, streaming hubs such as the streaming hubs 5420, 5480 may effectively move the electronics required for streaming to an external hub via cable. It will be appreciated in light of the disclosure that the streaming hubs may be located virtually next to the streaming sensors or up to a distance supported by the electronic driving capability of the hub. In instances where an internet cache protocol (“ICP”) is used, the distance supported by the electronic driving capability of the hub would be anywhere from 100 to 1000 feet (30.5 to 305 meters) based on desired frequency response, cable capacitance, and the like. In embodiments, the streaming hubs may be positioned in a location convenient for receiving power as well as connecting to a network (be it LAN or WAN). In embodiments, other power options would include solar, thermal as well as energy harvesting. Transfer between the streaming sensors and any external systems may be wireless or wired and may include such standard communication technologies as 802.11 and 900 MHz wireless systems, Ethernet, USB, firewire and so on.

[0463] With reference to FIG. 22, the many examples of the DAQ instrument 5002 include embodiments where data that may be uploaded from the local data control application 5062 to the master raw data server (“MRDS”) 5082. In embodiments, information in the multimedia probe (“MMP”) and probe control, sequence and analytical (“PCSA”) information store 5040 may also be downloaded from the MRDS 5082 down to the DAQ instrument 5002. Further details of the MRDS 5082 are shown in FIG. 32 including embodiments where data may be transferred to the MRDS 5082 from the DAQ instrument 5002 via a wired or wireless network, or through connection to one or more portable media, drive, other network connections, or the like. In embodiments, the DAQ instrument 5002 may be configured to be portable and may be carried on one or more predetermined routes to assess predefined points of measurement. In these many examples, the operating system that may be included in the MRDS 5082 may be Windows™, Linux™, or MacOS™ operating systems, or other similar operating systems. Further, in these arrangements, the operating system, modules for the operating system, and other needed libraries, data storage, and the like may be accessible wholly or partially through access to the cloud network facility 5080. In embodiments, the MRDS 5082 may reside directly on the DAQ instrument 5002, especially in on-line system examples. In embodiments, the DAQ instrument 5002 may be linked on an intra-network in a facility but may otherwise be behind a firewall. In further examples, the DAQ instrument 5002 may be linked to the cloud network facility 5080. In the various embodiments, one of the computers or mobile computing devices may be effectively designated the MRDS 5082 to which all of the other computing devices may feed it data such as one of the MRDS 6104, as depicted in FIGS. 41 and 42. In the many examples where the DAQ instrument 5002 may be deployed and configured to receive stream data in a swarm environment, one or more of the DAQ instruments 5002 may be effectively designated the MRDS 5082 to which all of the other computing devices may feed it data. In the many examples where the DAQ instrument 5002 may be deployed and configured to receive stream data in an environment where the methods and systems disclosed herein are intelligently assigning, controlling, adjusting, and re-adjusting data pools, computing resources, network bandwidth for local data collection, and the like, one or more of the DAQ instruments 5002 may be effectively designated the MRDS 5082 to which all of the other computing devices may feed it data.

[0464] With further reference to FIG. 32, new raw streaming data, data that have been through extract, process, and align processes (EP data), and the like may be uploaded to one or more master raw data servers as needed or as scaled in various environments. In embodiments, a master raw data server (“MRDS”) 5700 may connect to and receive data from other master raw data servers such as the MRDS 5082. The MRDS 5700 may include a data distribution manager module 5702. In embodiments, the new raw streaming data may be stored in the new stream data repository 5704. In many instances, like raw data streams stored on the DAQ instrument 5002, the new stream data repository 5704 and new extract and process data repository 5708 may be similarly configured as a temporary storage area.

[0465] In embodiments, the MRDS 5700 may include a stream data analyzer module with an extract and process alignment module 5710. The analyzer module 5710 may be shown to be a more robust data analyzer and extractor than may be typically found on portable streaming DAQ instruments although it may be deployed on the DAQ instrument 5002 as well. In embodiments, the analyzer module 5710 takes streaming data and instantiates it at a specific sampling rate and resolution similar to the local data control module 5062 on the DAQ instrument 5002. The specific sampling rate and resolution of the analyzer module 5710 may be based on either user input 5712 or automated extractions from a multimedia probe (“MMP”) and the probe control, sequence and analytical (“PCSA”) information store 5714 and/or an identification mapping table 5718, which may require the user input 5712 if there is incomplete information regarding various forms of legacy data similar to as was detailed with the DAQ instrument 5002. In embodiments, legacy data may be processed with the analyzer module 5710 and may be stored in one or more temporary holding areas such as a new legacy data repository 5722. One or more temporary areas may be configured to hold data until it is copied to an archive and verified. The analyzer 5710 module may also facilitate in-depth analysis by providing many varying types of signal processing tools including but not limited to filtering, Fourier transforms, weighting, resampling, envelope demodulation, wavelets, two-channel analysis, and the like. From this analysis, many different types of plots and mini-reports may be generated from a reports and plots module 5724. In embodiments, data is sent to the processing, analysis, reports, and archiving (“PARA”) server 5730 upon user initiation or in an automated fashion especially for on-line systems.

[0466] Referring to FIGS. 33-34, in embodiments, a PARA server 5750 may connect to and receive data from other PARA servers such as the PARA server 5730. With reference to FIG. 34, the PARA server 5730 may provide data to a supervisory module 5752 on the PARA server 5750 that may be configured to provide at least one of processing, analysis, reporting, archiving, supervisory, and similar functionalities. The supervisory module 5752 may also contain extract, process align functionality and the like. In embodiments, incoming streaming data may first be stored in a raw data stream archive 5760 after being properly validated. Based on the analytical requirements derived from a multimedia probe (“MMP”) and probe control, sequence and analytical (“PCSA”) information store 5762 as well as user settings, data may be extracted, analyzed, and stored in an extract and process (“EP”) raw data archive 5764. In embodiments, various reports from a reports module 5768 are generated from the supervisory module 5752. The various reports from the reports module 5768 include trend plots of various smart bands, overalls along with statistical patterns, and the like. In embodiments, the reports module 5768 may also be configured to compare incoming data to historical data. By way of these examples, the reports module 5768 may search for and analyze adverse trends, sudden changes, machinery defect patterns, and the like. In embodiments, the PARA server 5750 may include an expert analysis module 5770 from which reports are generated and analysis may be conducted. Upon completion, archived data may be fed to a local master server (“LMS”) 5772 via a server module 5774 that may connect to the local area network. In embodiments, archived data may also be fed to the LMS 5772 via a cloud data management server (“CDMS”) 5778 through a server module for a cloud network facility 5080. In embodiments, the supervisory module 5752 on the PARA server 5750 may be configured to provide at least one of processing, analysis, reporting, archiving, supervisory, and similar functionalities from which alarms may be generated, rated, stored, modified, reassigned, and the like with an alarm generator module 5782.

[0467] FIG. 34 depicts various embodiments that include a PARA server 5800 and its connection to LAN 5802. In embodiments, one or more DAQ instruments such as the DAQ instrument 5002 may receive and process analog data from one or more analog sensors 5710 that may be fed into the DAQ instrument 5002. As discussed herein, the DAQ instrument 5002 may create a digital stream of data based on the ingested analog data from the one or more analog sensors. The digital stream from the DAQ instrument 5002 may be uploaded to the MRDS 5082 and from there, it may be sent to the PARA server 5800 where multiple terminals, such as terminal 5810 5812, 5814, may each interface with it or the MRDS 5082 and view the data and/or analysis reports. In embodiments, the PARA server 5800 may communicate with a network data server 5820 that may include a LMS 5822. In these examples, the LMS 5822 may be configured as an optional storage area for archived data. The LMS 5822 may also be configured as an external driver that may be connected to a PC or other computing device that may run the LMS 5822; or the LMS 5822 may be directly run by the PARA server 5800 where the LMS 5822 may be configured to operate and coexist with the PARA server 5800. The LMS 5822 may connect with a raw data stream archive 5824, an extract and process (“EP”) raw data archive 5828, and a MMP and probe control, sequence and analytical (“PCSA”) information store 5830. In embodiments, a CDMS 5832 may also connect to the LAN 5802 and may also support the archiving of data.

[0468] In embodiments, portable connected devices 5850 such as a tablet 5852 and a smart phone 5854 may connect the CDMS 5832 using web APIs 5860 and 5862, respectively, as depicted in FIG. 35. The APIs 5860, 5862 may be configured to execute in a browser and may permit access via a cloud network facility 5080 of all (or some of) the functions previously discussed as accessible through the PARA Server 5800. In embodiments, computing devices of a user 5880 such as computing devices 5882, 5884, 5888 may also access the cloud network facility 5080 via a browser or other connection in order to receive the same functionality. In embodiments, thin-client apps which do not require any other device drivers and may be facilitated by web services supported by cloud services 5890 and cloud data 5892. In many examples, the thin-client apps may be developed and reconfigured using, for example, the visual high-level LabVIEW™ programming language with NXG™ Web-based virtual interface subroutines. In embodiments, thin client apps may provide high-level graphing functions such as those supported by LabVIEW™ tools. In embodiments, the LabVIEW™ tools may generate JSCRIPT™ code and JAVA™ code that may be edited post-compilation. The NXG™ tools may generate Web VI's that may not require any specialized driver and only some RESTful™ services which may be readily installed from any browser. It will be appreciated in light of the disclosure that because various applications may be run inside a browser, the applications may be run on any operating system, such as Windows™, Linux™, and Android™ operating systems especially for personal devices, mobile devices, portable connected devices, and the like.

[0469] In embodiments, the CDMS 5832 is depicted in greater detail in FIG. 36. In embodiments, the CDMS 5832 may provide all of the data storage and services that the PARA Server 5800 (FIG. 34) may provide. In contrast, all of the API's may be web API's which may run in a browser and all other apps may run on the PARA Server 5800 or the DAQ instrument 5002 which may typically be Windows™, Linux™ or other similar operating systems. In embodiments, the CDMS 5832 includes at least one of or combinations of the following functions: the CDMS 5832 may include a cloud GUI 5900 that may be configured to provide access to all data plots including trend, waveform, spectra, envelope, transfer function, logs of measurement events, analysis including expert, utilities, and the like. In embodiments, the CDMS 5832 may include a cloud data exchange 5902 configured to facilitate the transfer of data to and from the cloud network facility 5080. In embodiments, the CDMS 5832 may include a cloud plots/trends module 5904 that may be configured to show all plots via web apps including trend, waveform, spectra, envelope, transfer function, and the like. In embodiments, the CDMS 5832 may include a cloud reporter 5908 that may be configured to provide all analysis reports, logs, expert analysis, trend plots, statistical information, and the like. In embodiments, the CDMS 5832 may include a cloud alarm module 5910. Alarms from the cloud alarm module 5910 may be generated and may be sent to various devices 5920 via email, texts, or other messaging mechanisms. From the various modules, data may be stored in new data 5914. The various devices 5920 may include a terminal 5922, portable connected device 5924, or a tablet 5928. The alarms from the cloud alarm module are designed to be interactive so that the end user may acknowledge alarms in order to avoid receiving redundant alarms and also to see significant context-sensitive data from the alarm points that may include spectra, waveform statistical info, and the like.

[0470] In embodiments, a relational database server (“RDS”) 5930 may be used to access all of the information from a MMP and PCSA information store 5932. As with the PARA server 5800 (FIG. 36), information from the information store 5932 may be used with an EP and align module 5934, a data exchange 5938 and the expert system 5940. In embodiments, a raw data stream archive 5942 and extract and process raw data archive 5944 may also be used by the EP align 5934, the data exchange 5938 and the expert system 5940 as with the PARA server 5800. In embodiments, new stream raw data 5950, new extract and process raw data 5952, and new data 5954 (essentially all other raw data such as overalls, smart bands, stats, and data from the information store 5932) are directed by the CDMS 5832.

[0471] In embodiments, the streaming data may be linked with the RDS 5930 and the MMP and PCSA information store 5932 using a technical data management streaming (“TDMS”) file format. In embodiments, the information store 5932 may include tables for recording at least portions of all measurement events. By way of these examples, a measurement event may be any single data capture, a stream, a snapshot, an averaged level, or an overall level. Each of the measurement events in addition to point identification information may also have a date and time stamp. In embodiments, a link may be made between the streaming data, the measurement event, and the tables in the information store 5932 using the TDMS format. By way of these examples, the link may be created by storing unique measurement point identification codes with a file structure having the TDMS format by including and assigning TDMS properties. In embodiments, a file with the TDMS format may allow for three levels of hierarchy. By way of these examples, the three levels of hierarchy may be root, group, and channel. It will be appreciated in light of the disclosure that the Mimosa™ database schema may be, in theory, unlimited. With that said, there are advantages to limited TDMS hierarchies. In the many examples, the following properties may be proposed for adding to the TDMS Stream structure while using a Mimosa Compatible database schema.

[0472] Root Level: Global ID 1: Text String (This could be a unique ID obtained from the web); Global ID 2: Text String (This could be an additional ID obtained from the web); Company Name: Text String; Company ID: Text String; Company Segment ID: 4-byte Integer; Company Segment ID: 4-byte Integer; Site Name: Text String; Site Segment ID: 4-byte Integer; Site Asset ID: 4-byte Integer; Route Name: Text String; Version Number: Text String.

[0473] Group Level: Section 1 Name: Text String; Section 1 Segment ID: 4-byte Integer; Section 1 Asset ID: 4-byte Integer; Section 2 Name: Text String; Section 2 Segment ID: 4-byte Integer; Section 2 Asset ID: 4-byte Integer; Machine Name: Text String; Machine Segment ID: 4-byte Integer; Machine Asset ID: 4-byte Integer; Equipment Name: Text String; Equipment Segment ID: 4-byte Integer; Equipment Asset ID: 4-byte Integer; Shaft Name: Text String; Shaft Segment ID: 4-byte Integer; Shaft Asset ID: 4-byte Integer; Bearing Name: Text String; Bearing Segment ID: 4-byte Integer; Bearing Asset ID: 4-byte Integer; Probe Name: Text String; Probe Segment ID: 4-byte Integer; Probe Asset ID: 4-byte Integer.

[0474] Channel Level: Channel #: 4-byte Integer; Direction: 4-byte Integer (in certain examples may be text); Data Type: 4-byte Integer; Reserved Name 1: Text String; Reserved Segment ID 1: 4-byte Integer; Reserved Name 2: Text String; Reserved Segment ID 2: 4-byte Integer; Reserved Name 3: Text String; Reserved Segment ID 3: 4-byte Integer.

[0475] In embodiments, the file with the TDMS format may automatically use property or asset information and may make an index file out of the specific property and asset information to facilitate database searches, may offer a compromise for storing voluminous streams of data because it may be optimized for storing binary streams of data but may also include some minimal database structure making many standard SQL operations feasible, but the TDMS format and functionality discussed herein may not be as efficient as a full-fledged SQL relational database. The TDMS format, however, may take advantage of both worlds in that it may balance between the class or format of writing and storing large streams of binary data efficiently and the class or format of a fully relational database, which facilitates searching, sorting and data retrieval. In embodiments, an optimum solution may be found in that metadata required for analytical purposes and extracting prescribed lists with panel conditions for stream collection may be stored in the RDS 5930 by establishing a link between the two database methodologies. By way of these examples, relatively large analog data streams may be stored predominantly as binary storage in the raw data stream archive 5942 for rapid stream loading but with inherent relational SQL type hooks, formats, conventions, or the like. The files with the TDMS format may also be configured to incorporate DIAdem™ reporting capability of LabVIEW™ software in order to provide a further mechanism to conveniently and rapidly facilitate accessing the analog or the streaming data.

[0476] The methods and systems disclosed herein may include, connect to, or be integrated with a virtual data acquisition instrument and in the many embodiments, FIG. 37 shows methods and systems that include a virtual streaming DAQ instrument 6000 also known as a virtual DAQ instrument, a VRDS, or a VSDAQ. In contrast to the DAQ instrument 5002 (FIG. 22), the virtual DAQ instrument 6000 may be configured so to only include one native application. In the many examples, the one permitted and one native application may be the DAQ driver module 6002 that may manage all communications with the DAQ Device 6004 which may include streaming capabilities. In embodiments, other applications, if any, may be configured as thin client web applications such as RESTful™ web services. The one native application, or other applications or services, may be accessible through the DAQ Web API 6010. The DAQ Web API 6010 may run in or be accessible through various web browsers.

[0477] In embodiments, storage of streaming data, as well as the extraction and processing of streaming data into extract and process data, may be handled primarily by the DAQ driver services 6012 under the direction of the DAQ Web API 6010. In embodiments, the output from sensors of various types including vibration, temperature, pressure, ultrasound and so on may be fed into the instrument inputs of the DAQ device 6004. In embodiments, the signals from the output sensors may be signal conditioned with respect to scaling and filtering and digitized with an analog to a digital converter. In embodiments, the signals from the output sensors may be signals from all relevant channels simultaneously sampled at a rate sufficient to perform the maximum desired frequency analysis. In embodiments, the signals from the output sensors may be sampled for a relatively long time, gap-free, as one continuous stream so as to enable a wide array of further post-processing at lower sampling rates with sufficient samples. In further examples, streaming frequency may be adjusted (and readjusted) to record streaming data at non-evenly spaced recording. For temperature data, pressure data, and other similar data that may be relatively slow, varying delta times between samples may further improve quality of the data. By way of the above examples, data may be streamed from a collection of points and then the next set of data may be collected from additional points according to a prescribed sequence, route, path, or the like. In the many examples, the portable sensors may be moved to the next location according to the prescribed sequence but not necessarily all of them as some may be used for reference phase or otherwise. In further examples, a multiplexer 6020 may be used to switch to the next collection of points or a mixture of the two methods may be combined.

[0478] In embodiments, the sequence and panel conditions that may be used to govern the data collection process using the virtual DAQ instrument 6000 may be obtained from the MMP PCSA information store 6022. The MMP PCSA information store 6022 may include such items as the hierarchical structural relationships of the machine, i.e., a machine contains pieces of equipment in which each piece of equipment contains shafts and each shaft is associated with bearings, which may be monitored by specific types of transducers or probes according to a specific prescribed sequence (routes, path, etc.) with specific panel conditions. By way of these examples, the panel conditions may include hardware specific switch settings or other collection parameters such as sampling rate, AC/DC coupling, voltage range and gain, integration, high and low pass filtering, anti-aliasing filtering, ICP™ transducers and other integrated-circuit piezoelectric transducers, 4-20 mA loop sensors, and the like. The information store 6022 includes other information that may be stored in what would be machinery specific features that would be important for proper analysis including the number of gear teeth for a gear, the number of blades in a pump impeller, the number of motor rotor bars, bearing specific parameters necessary for calculating bearing frequencies, lx rotating speed (RPMs) of all rotating elements, and the like.

[0479] Upon direction of the DAQ Web API 6010 software, digitized waveforms may be uploaded using the DAQ driver services 6012 of the virtual DAQ instrument 6000. In embodiments, data may then be fed into an RLN data and control server 6030 that may store the stream data into a network stream data repository 6032. Unlike the DAQ instrument 5002, the server 6030 may run from within the DAQ driver module 6002. It will be appreciated in light of the disclosure that a separate application may require drivers for running in the native operating system and for this instrument only the instrument driver may run natively. In many examples, all other applications may be configured to be browser based. As such, a relevant network variable may be very similar to a LabVIEW™ shared or network stream variable which may be designed to be accessed over one or more networks or via web applications.

[0480] In embodiments, the DAQ web API 6010 may also direct the local data control application 6034 to extract and process the recently obtained streaming data and, in turn, convert it to the same or lower sampling rates of sufficient length to provide the desired resolution. This data may be converted to spectra, then averaged and processed in a variety of ways and stored as EP data, such as on an EP data repository 6040. The EP data repository 6040 may, in certain embodiments, only be meant for temporary storage. It will be appreciated in light of the disclosure that legacy data may require its own sampling rates and resolution and often this sampling rate may not be integer proportional to the acquired sampling rate especially for order-sampled data whose sampling frequency is related directly to an external frequency. The external frequency may typically be the running speed of the machine or its internal componentry, rather than the more-standard sampling rates produced by the internal crystals, clock functions, and the like of the (e.g., values of Fmax of 100, 200, 500, 1K, 2K, 5K, 10K, 20K and so on) of the DAQ instrument 5002, 6000. In embodiments, the EP align component of the local data control application 6034 is able to fractionally adjust the sampling rate to the non-integer ratio rates that may be more applicable to legacy data sets and therefore drive compatibility with legacy systems. In embodiments, the fractional rates may be converted to integer ratio rates more readily because the length of the data to be processed (or at least that portion of the greater stream of data) is adjustable because of the depth and content of the original acquired streaming data by the DAQ instrument 5002, 6000. It will be appreciated in light of the disclosure that if the data was not streamed and just stored as traditional snap-shots of spectra with the standard values of Fmax, it may very well be impossible to retroactively and accurately convert the acquired data to the order-sampled data. In embodiments, the stream data may be converted, especially for legacy data purposes, to the proper sampling rate and resolution as described and stored in the EP legacy data repository 6042. To support legacy data identification scenarios, a user input 6044 may be included if there is no automated process for identification translation. In embodiments, one such automated process for identification translation may include importation of data from a legacy system that may contain a fully standardized format such as the Mimosa™ format and sufficient identification information to complete an ID Mapping Table 6048. In further examples, the end user, a legacy data vendor, a legacy data storage facility, or the like may be able to supply enough info to complete (or sufficiently complete) relevant portions of the ID Mapping Table 6048 to provide, in turn, the database schema for the raw data of the legacy system so it may be readily ingested, saved, and used for analytics in the current systems disclosed herein.

[0481] FIG. 38 depicts further embodiments and details of the virtual DAQ Instrument 6000. In these examples, the DAQ Web API 6010 may control the data collection process as well as its sequence. The DAQ Web API 6010 may provide the capability for editing this process, viewing plots of the data, controlling the processing of that data and viewing the output in all its myriad forms, analyzing the data, including the expert analysis, communicating with external devices via the DAQ driver module 6002, as well as communicating with and transferring both streaming data and EP data to one or more cloud network facilities 5080 whenever possible. In embodiments, the virtual DAQ instrument itself and the DAQ Web API 6010 may run independently of access to cloud network facilities 5080 when local demands may require or simply as a result of there being no outside connectivity such use throughout a proprietary industrial setting that prevents such signals. In embodiments, the DAQ Web API 6010 may also govern the movement of data, its filtering, as well as many other housekeeping functions.

[0482] The virtual DAQ Instrument 6000 may also include an expert analysis module 6052. In embodiments, the expert analysis module 6052 may be a web application or other suitable module that may generate reports 6054 that may use machine or measurement point specific information from the MMP PCSA information store 6022 to analyze stream data 6058 using the stream data analyzer module 6050. In embodiments, supervisory control of the module 6052 may be provided by the DAQ Web API 6010. In embodiments, the expert analysis may also be supplied (or supplemented) via the expert system module 5940 that may be resident on one or more cloud network facilities that are accessible via the CDMS 5832. In many examples, expert analysis via the cloud may be preferred over local systems such as expert analysis module 6052 for various reasons, such as the availability and use of the most up-to-date software version, more processing capability, a bigger volume of historical data to reference and the like. It will be appreciated in light of the disclosure that it may be important to offer expert analysis when an internet connection cannot be established so as to provide a redundancy, when needed, for seamless and time efficient operation. In embodiments, this redundancy may be extended to all of the discussed modular software applications and databases where applicable so each module discussed herein may be configured to provide redundancy to continue operation in the absence of an internet connection.

[0483] FIG. 39 depicts further embodiments and details of many virtual DAQ instruments existing in an online system and connecting through network endpoints through a central DAQ instrument to one or more cloud network facilities. In embodiments, a master DAQ instrument with network endpoint 6060 is provided along with additional DAQ instruments such as a DAQ instrument with network endpoint 6062, a DAQ instrument with network endpoint 6064, and a DAQ instrument with network endpoint 6068. The master DAQ instrument with network endpoint 6060 may connect with the other DAQ instruments with network endpoints 6062, 6064, 6068 over LAN 6070. It will be appreciated that each of the instruments 6060, 6062, 6064, 6068 may include personal computer, a connected device, or the like that include Windows™, Linux™, or other suitable operating systems to facilitate ease of connection of devices utilizing many wired and wireless network options such as Ethernet, wireless 802.11g, 900 MHz wireless (e.g., for better penetration of walls, enclosures and other structural barriers commonly encountered in an industrial setting), as well as a myriad of other things permitted by the use of off-the-shelf communication hardware when needed.

[0484] FIG. 40 depicts further embodiments and details of many functional components of an endpoint that may be used in the various settings, environments, and network connectivity settings. The endpoint includes endpoint hardware modules 6080. In embodiments, the endpoint hardware modules 6080 may include one or more multiplexers 6082, a DAQ instrument 6084, as well as a computer 6088, computing device, PC, or the like that may include the multiplexers, DAQ instruments, and computers, connected devices and the like, as disclosed herein. The endpoint software modules 6090 include a data collector application (DCA) 6092 and a raw data server (RDS) 6094. In embodiments, DCA 6092 may be similar to the DAQ API 5052 (FIG. 22) and may be configured to be responsible for obtaining stream data from the DAQ device 6084 and storing it locally according to a prescribed sequence or upon user directives. In the many examples, the prescribed sequence or user directives may be a LabVIEW™ software app that may control and read data from the DAQ instruments. For cloud-based online systems, the stored data in many embodiments may be network accessible. In many examples, LabVIEW™ tools may be used to accomplish this with a shared variable or network stream (or subsets of shared variables). Shared variables and the affiliated network streams may be network objects that may be optimized for sharing data over the network. In many embodiments, the DCA 6092 may be configured with a graphic user interface that may be configured to collect data as efficiently and fast as possible and push it to the shared variable and its affiliated network stream. In embodiments, the endpoint raw data server 6094 may be configured to read raw data from the single-process shared variable and may place it with a master network stream. In embodiments, a raw stream of data from portable systems may be stored locally and temporarily until the raw stream of data is pushed to the MRDS 5082 (FIG. 22). It will be appreciated in light of the disclosure that on-line system instruments on a network can be termed endpoints whether local or remote or associated with a local area network or a wide area network. For portable data collector applications that may or may not be wirelessly connected to one or more cloud network facilities, the endpoint term may be omitted as described so as to detail an instrument that may not require network connectivity.

[0485] FIG. 41 depicts further embodiments and details of multiple endpoints with their respective software blocks with at least one of the devices configured as master blocks. Each of the blocks may include a data collector application (“DCA”) 6100 and a raw data server (“RDS”) 6102. In embodiments, each of the blocks may also include a master raw data server module (“MRDS”) 6104, a master data collection and analysis module (“MDCA”) 6108, and a supervisory and control interface module (“SCI”) 6110. The MRDS 6104 may be configured to read network stream data (at a minimum) from the other endpoints and may forward it up to one or more cloud network facilities via the CDMS 5832 including the cloud services 5890 and the cloud data 5892. In embodiments, the CDMS 5832 may be configured to store the data and to provide web, data, and processing services. In these examples, this may be implemented with a LabVIEW™ application that may be configured to read data from the network streams or share variables from all of the local endpoints, write them to the local host PC, local computing device, connected device, or the like, as both a network stream and file with TDMS™ formatting. In embodiments, the CDMS 5832 may also be configured to then post this data to the appropriate buckets using the LabVIEW or similar software that may be supported by S3™ web service from the Amazon Web Services (“AWS™”) on the Amazon™ web server, or the like and may effectively serve as a back-end server. In the many examples, different criteria may be enabled or may be set up for when to post data, create or adjust schedules, create or adjust event triggering including a new data event, create a buffer full message, create or more alarms messages, and the like.

[0486] In embodiments, the MDCA 6108 may be configured to provide automated as well as user-directed analyses of the raw data that may include tracking and annotating specific occurrence and in doing so, noting where reports may be generated and alarms may be noted. In embodiments, the SCI 6110 may be an application configured to provide remote control of the system from the cloud as well as the ability to generate status and alarms. In embodiments, the SCI 6110 may be configured to connect to, interface with, or be integrated into a supervisory control and data acquisition (“SCADA”) control system. In embodiments, the SCI 6110 may be configured as a LabVIEW™ application that may provide remote control and status alerts that may be provided to any remote device that may connect to one or more of the cloud network facilities 5080.

[0487] In embodiments, the equipment that is being monitored may include RFID tags that may provide vital machinery analysis background information. The RFID tags may be associated with the entire machine or associated with the individual componentry and may be substituted when certain parts of the machine are replaced, repaired, or rebuilt. The RFID tags may provide permanent information relevant to the lifetime of the unit or may also be re-flashed to update with at least a portion of new information. In many embodiments, the DAQ instruments 5002 disclosed herein may interrogate the one or more RFID chips to learn of the machine, its componentry, its service history, and the hierarchical structure of how everything is connected including drive diagrams, wire diagrams, and hydraulic layouts. In embodiments, some of the information that may be retrieved from the RFID tags includes manufacturer, machinery type, model, serial number, model number, manufacturing date, installation date, lots numbers, and the like. By way of these examples, machinery type may include the use of a Mimosa™ format table including information about one or more of the following motors, gearboxes, fans, and compressors. The machinery type may also include the number of bearings, their type, their positioning, and their identification numbers. The information relevant to one or more fans includes fan type, number of blades, number of vanes, and number of belts. It will be appreciated in light of the disclosure that other machines and their componentry may be similarly arranged hierarchically with relevant information all of which may be available through interrogation of one or more RFID chips associated with the one or more machines.

[0488] In embodiments, data collection in an industrial environment may include routing analog signals from a plurality of sources, such as analog sensors, to a plurality of analog signal processing circuits. Routing of analog signals may be accomplished by an analog crosspoint switch that may route any of a plurality of analog input signals to any of a plurality of outputs, such as to analog and/or digital outputs. Routing of inputs to outputs in an analog signal crosspoint switch in an industrial environment may be configurable, such as by an electronic signal to which a switch portion of the analog crosspoint switch is responsive.

[0489] In embodiments, the analog crosspoint switch may receive analog signals from a plurality of analog signal sources in the industrial environment. Analog signal sources may include sensors that produce an analog signal. Sensors that produce an analog signal that may be switched by the analog crosspoint switch may include sensors that detect a condition and convert it to an analog signal that may be representative of the condition, such as converting a condition to a corresponding voltage. Exemplary conditions that may be represented by a variable voltage may include temperature, friction, sound, light, torque, revolutions-per-minute, mechanical resistance, pressure, flow rate, and the like, including any of the conditions represented by inputs sources and sensors disclosed throughout this disclosure and the documents incorporated herein by reference. Other forms of analog signal may include electrical signals, such as variable voltage, variable current, variable resistance, and the like.

[0490] In embodiments, the analog crosspoint switch may preserve one or more aspects of an analog signal being input to it in an industrial environment. Analog circuits integrated into the switch may provide buffered outputs. The analog circuits of the analog crosspoint switch may follow an input signal, such as an input voltage to produce a buffered representation on an output. This may alternatively be accomplished by relays (mechanical, solid state, and the like) that allow an analog voltage or current present on an input to propagate to a selected output of the analog switch.

[0491] In embodiments, an analog crosspoint switch in an industrial environment may be configured to switch any of a plurality of analog inputs to any of a plurality of analog outputs. An example embodiment includes a MIMO, multiplexed configuration. An analog crosspoint switch may be dynamically configurable so that changes to the configuration causes a change in the mapping of inputs to outputs. A configuration change may apply to one or more mappings so that a change in mapping may result in one or more of the outputs being mapped to different input than before the configuration change.

[0492] In embodiments, the analog crosspoint switch may have more inputs than outputs, so that only a subset of inputs can be routed to outputs concurrently. In other embodiments, the analog crosspoint switch may have more outputs than inputs, so that either a single input may be made available currently on multiple outputs, or at least one output may not be mapped to any input.

[0493] In embodiments, an analog crosspoint switch in an industrial environment may be configured to switch any of a plurality of analog inputs to any of a plurality of digital outputs. To accomplish conversion from analog inputs to digital outputs, an analog-to-digital converter circuit may be configured on each input, each output, or at intermediate points between the input(s) and output(s) of the analog crosspoint switch. Benefits of including digitization of analog signals in an analog crosspoint switch that may be located close to analog signal sources may include reducing signal transport costs and complexity that digital signal communication has over analog, reducing energy consumption, facilitating detection and regulation of aberrant conditions before they propagate throughout an industrial environment, and the like. Capturing analog signals close to their source may also facilitate improved signal routing management that is more tolerant of real world effects such as requiring that multiple signals be routed simultaneously. In this example, a portion of the signals can be captured (and stored) locally while another portion can be transferred through the data collection network. Once the data collection network has available bandwidth, the locally stored signals can be delivered, such as with a time stamp indicating the time at which the data was collected. This technique may be useful for applications that have concurrent demand for data collection channels that exceed the number of channels available. Sampling control may also be based on an indication of data worth sampling. As an example, a signal source, such as a sensor in an industrial environment may provide a data valid signal that transmits an indication of when data from the sensor is available.

[0494] In embodiments, mapping inputs of the analog crosspoint switch to outputs may be based on a signal route plan for a portion of the industrial environment that may be presented to the crosspoint switch. The signal route plan may be used in a method of data collection in the industrial environment that may include routing a plurality of analog signals along a plurality of analog signal paths. The method may include connecting the plurality of analog signals individually to inputs of the analog crosspoint switch that may be configured with a route plan. The crosspoint switch may, responsively to the configured route plan, route a portion of the plurality of analog signals to a portion of the plurality of analog signal paths.

[0495] In embodiments, the analog crosspoint switch may include at least one high current output drive circuit that may be suitable for routing the analog signal along a path that requires high current. In embodiments, the analog crosspoint switch may include at least one voltage-limited input that may facilitate protecting the analog crosspoint switch from damage due to excessive analog input signal voltage. In embodiments, the analog crosspoint switch may include at least one current limited input that may facilitate protecting the analog crosspoint switch from damage due to excessive analog input current. The analog crosspoint switch may comprise a plurality of interconnected relays that may facilitate routing the input(s) to the output(s) with little or no substantive signal loss.

[0496] In embodiments, an analog crosspoint switch may include processing functionality, such as signal processing and the like (e.g., a programmed processor, special purpose processor, a digital signal processor, and the like) that may detect one or more analog input signal conditions. In response to such detection, one or more actions may be performed, such as setting an alarm, sending an alarm signal to another device in the industrial environment, changing the crosspoint switch configuration, disabling one or more outputs, powering on or off a portion of the switch, changing a state of an output, such as a general purpose digital or analog output, and the like. In embodiments, the switch may be configured to process inputs for producing a signal on one or more of the outputs. The inputs to use, processing algorithm for the inputs, condition for producing the signal, output to use, and the like may be configured in a data collection template.

[0497] In embodiments, an analog crosspoint switch may comprise greater than 32 inputs and greater than 32 outputs. A plurality of analog crosspoint switches may be configured so that even though each switch offers fewer than 32 inputs and 32 outputs it may be configured to facilitate switching any of 32 inputs to any of 32 outputs spread across the plurality of crosspoint switches.

[0498] In embodiments, an analog crosspoint switch suitable for use in an industrial environment may comprise four or fewer inputs and four or fewer outputs. Each output may be configurable to produce an analog output that corresponds to the mapped analog input or it may be configured to produce a digital representation of the corresponding mapped input.

[0499] In embodiments, an analog crosspoint switch for use in an industrial environment may be configured with circuits that facilitate replicating at least a portion of attributes of the input signal, such as current, voltage range, offset, frequency, duty cycle, ramp rate, and the like while buffering (e.g., isolating) the input signal from the output signal. Alternatively, an analog crosspoint switch may be configured with unbuffered inputs/outputs, thereby effectively producing a bi-directional based crosspoint switch).

[0500] In embodiments, an analog crosspoint switch for use in an industrial environment may include protected inputs that may be protected from damaging conditions, such as through use of signal conditioning circuits. Protected inputs may prevent damage to the switch and to downstream devices to which the switch outputs connect. As an example, inputs to such an analog crosspoint switch may include voltage clipping circuits that prevent a voltage of an input signal from exceeding an input protection threshold. An active voltage adjustment circuit may scale an input signal by reducing it uniformly so that a maximum voltage present on the input does not exceed a safe threshold value. As another example, inputs to such an analog crosspoint switch may include current shunting circuits that cause current beyond a maximum input protection current threshold to be diverted through protection circuits rather than enter the switch. Analog switch inputs may be protected from electrostatic discharge and/or lightning strikes. Other signal conditioning functions that may be applied to inputs to an analog crosspoint switch may include voltage scaling circuitry that attempts to facilitate distinguishing between valid input signals and low voltage noise that may be present on the input. However, in embodiments, inputs to the analog crosspoint switch may be unbuffered and/or unprotected to make the least impact on the signal. Signals such as alarm signals, or signals that cannot readily tolerate protection schemes, such as those schemes described above herein may be connected to unbuffered inputs of the analog crosspoint switch.

[0501] In embodiments, an analog crosspoint switch may be configured with circuitry, logic, and/or processing elements that may facilitate input signal alarm monitoring. Such an analog crosspoint switch may detect inputs meeting alarm conditions and in response thereto, switch inputs, switch mapping of inputs to outputs, disable inputs, disable outputs, issue an alarm signal, activate/deactivate a general-purpose output, or the like.

[0502] In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to selectively power up or down portions of the analog crosspoint switch or circuitry associated with the analog crosspoint switch, such as input protection devices, input conditioning devices, switch control devices and the like. Portions of the analog crosspoint switch that may be powered on/off may include outputs, inputs, sections of the switch and the like. In an example, an analog crosspoint switch may include a modular structure that may separate portions of the switch into independently powered sections. Based on conditions, such as an input signal meeting a criterion or a configuration value being presented to the analog crosspoint switch, one or more modular sections may be powered on/off.

[0503] In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to perform signal processing including, without limitation, providing a voltage reference for detecting an input crossing the voltage reference (e.g., zero volts for detecting zero-crossing signals), a phase-lock loop to facilitate capturing slow frequency signals (e.g., low-speed revolution-per-minute signals and detecting their corresponding phase), deriving input signal phase relative to other inputs, deriving input signal phase relative to a reference (e.g., a reference clock), deriving input signal phase relative to detected alarm input conditions and the like. Other signal processing functions of such an analog crosspoint switch may include oversampling of inputs for delta-sigma A/D, to produce lower sampling rate outputs, to minimize AA filter requirements and the like. Such an analog crosspoint switch may support long block sampling at a constant sampling rate even as inputs are switched, which may facilitate input signal rate independence and reduce complexity of sampling scheme(s). A constant sampling rate may be selected from a plurality of rates that may be produced by a circuit, such as a clock divider circuit that may make available a plurality of components of a reference clock.

[0504] In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to support implementing data collection/data routing templates in the industrial environment. The analog crosspoint switch may implement a data collection/data routing template based on conditions in the industrial environment that it may detect or derive, such as an input signal meeting one or more criteria (e.g., transition of a signal from a first condition to a second, lack of transition of an input signal within a predefined time interface (e.g., inactive input) and the like).

[0505] In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to be configured from a portion of a data collection template. Configuration may be done automatically (without needing human intervention to perform a configuration action or change in configuration), such as based on a time parameter in the template and the like. Configuration may be done remotely, e.g., by sending a signal from a remote location that is detectable by a switch configuration feature of the analog crosspoint switch. Configuration may be done dynamically, such as based on a condition that is detectable by a configuration feature of the analog crosspoint switch (e.g., a timer, an input condition, an output condition, and the like). In embodiments, information for configuring an analog crosspoint switch may be provided in a stream, as a set of control lines, as a data file, as an indexed data set, and the like. In embodiments, configuration information in a data collection template for the switch may include a list of each input and a corresponding output, a list of each output function (active, inactive, analog, digital and the like), a condition for updating the configuration (e.g., an input signal meeting a condition, a trigger signal, a time (relative to another time/event/state, or absolute), a duration of the configuration, and the like. In embodiments, configuration of the switch may be input signal protocol aware so that switching from a first input to a second input for a given output may occur based on the protocol. In an example, a configuration change may be initiated with the switch to switch from a first video signal to a second video signal. The configuration circuitry may detect the protocol of the input signal and switch to the second video signal during a synchronization phase of the video signal, such as during horizontal or vertical refresh. In other examples, switching may occur when one or more of the inputs are at zero volts. This may occur for a sinusoidal signal that transitions from below zero volts to above zero volts.

[0506] In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to provide digital outputs by converting analog signals input to the switch into digital outputs. Converting may occur after switching the analog inputs based on a data collection template and the like. In embodiments, a portion of the switch outputs may be digital and a portion may be analog. Each output, or groups thereof, may be configurable as analog or digital, such as based on analog crosspoint switch output configuration information included in or derived from a data collection template. Circuitry in the analog crosspoint switch may sense an input signal voltage range and intelligently configure an analog-to-digital conversion function accordingly. As an example, a first input may have a voltage range of 12 volts and a second input may have a voltage range of 24 volts. Analog-to-digital converter circuits for these inputs may be adjusted so that the full range of the digital value (e.g., 256 levels for an 8-bit signal) will map substantially linearly to 12 volts for the first input and 24 volts for the second input.

[0507] In embodiments, an analog crosspoint switch may automatically configure input circuitry based on characteristics of a connected analog signal. Examples of circuitry configuration may include setting a maximum voltage, a threshold based on a sensed maximum threshold, a voltage range above and/or below a ground reference, an offset reference, and the like. The analog crosspoint switch may also adapt inputs to support voltage signals, current signals, and the like. The analog crosspoint switch may detect a protocol of an input signal, such as a video signal protocol, audio signal protocol, digital signal protocol, protocol based on input signal frequency characteristics, and the like. Other aspects of inputs of the analog crosspoint switch that may be adapted based on the incoming signal may include a duration of sampling of the signal, and comparator or differential type signals, and the like.

[0508] In embodiments, an analog crosspoint switch may be configured with functionality to counteract input signal drift and/or leakage that may occur when an analog signal is passed through it over a long period of time without changing value (e.g., a constant voltage). Techniques may include voltage boost, current injection, periodic zero referencing (e.g., temporarily connecting the input to a reference signal, such as ground, applying a high resistance pathway to the ground reference, and the like).

[0509] In embodiments, a system for data collection in an industrial environment may include an analog crosspoint switch deployed in an assembly line comprising conveyers and/or lifters. A power roller conveyor system includes many rollers that deliver product along a path. There may be many points along the path that may be monitored for proper operation of the rollers, load being placed on the rollers, accumulation of products, and the like. A power roller conveyor system may also facilitate moving product through longer distances and therefore may have a large number of products in transport at once. A system for data collection in such an assembly environment may include sensors that detect a wide range of conditions as well as at a large number of positions along the transport path. As a product progresses down the path, some sensors may be active and others, such as those that the product has passed may be inactive. A data collection system may use an analog crosspoint switch to select only those sensors that are currently or anticipated to be active by switching from inputs that connect to inactive sensors to those that connect to active sensors and thereby provide the most useful sensor signals to data detection and/or collection and/or processing facilities. In embodiments, the analog crosspoint switch may be configured by a conveyor control system that monitors product activity and instructs the analog crosspoint switch to direct different inputs to specific outputs based on a control program or data collection template associated with the assembly environment.

[0510] In embodiments, a system for data collection in an industrial environment may include an analog crosspoint switch deployed in a factory comprising use of fans as industrial components. In embodiments, fans in a factory setting may provide a range of functions including drying, exhaust management, clean air flow and the like. In an installation of a large number of fans, monitoring fan rotational speed, torque, and the like may be beneficial to detect an early indication of a potential problem with air flow being produced by the fans. However, concurrently monitoring each of these elements for a large number of fans may be inefficient. Therefore, sensors, such as tachometers, torque meters, and the like may be disposed at each fan and their analog output signal(s) may be provided to an analog crosspoint switch. With a limited number of outputs, or at least a limited number of systems that can process the sensor data, the analog crosspoint switch may be used to select among the many sensors and pass along a subset of the available sensor signals to data collection, monitoring, and processing systems. In an example, sensor signals from sensors disposed at a group of fans may be selected to be switched onto crosspoint switch outputs. Upon satisfactory collection and/or processing of the sensor signals for this group of fans, the analog crosspoint switch may be reconfigured to switch signals from another group of fans to be processed.

[0511] In embodiments, a system for data collection in an industrial environment may include an analog crosspoint switch deployed as an industrial component in a turbine-based power system. Monitoring for vibration in turbine systems, such as hydro-power systems, has been demonstrated to provide advantages in reduction in down time. However, with a large number of areas to monitor for vibration, particularly for on-line vibration monitoring, including relative shaft vibration, bearings absolute vibration, turbine cover vibration, thrust bearing axial vibration, stator core vibrations, stator bar vibrations, stator end winding vibrations, and the like, it may be beneficial to select among this list over time, such as taking samples from sensors for each of these types of vibration a few at a time. A data collection system that includes an analog crosspoint switch may provide this capability by connecting each vibration sensor to separate inputs of the analog crosspoint switch and configuring the switch to output a subset of its inputs. A vibration data processing system, such as a computer, may determine which sensors to pass through the analog crosspoint switch and configure an algorithm to perform the vibration analysis accordingly. As an example, sensors for capturing turbine cover vibration may be selected in the analog crosspoint switch to be passed on to a system that is configured with an algorithm to determine turbine cover vibration from the sensor signals. Upon completion of determining turbine cover vibration, the crosspoint switch may be configured to pass along thrust bearing axial vibration sensor signals and a corresponding vibration analysis algorithm may be applied to the data. In this way, each type of vibration may be analyzed by a single processing system that works cooperatively with an analog crosspoint switch to pass specific sensor signals for processing.

[0512] Referring to FIG. 44, an analog crosspoint switch for collecting data in an industrial environment is depicted. The analog crosspoint switch 7022 may have a plurality of inputs 7024 that connect to sensors 7026 in the industrial environment. The analog crosspoint switch 7022 may also comprise a plurality of outputs 7028 that connect to data collection infrastructure, such as analog-to-digital converters 7030, analog comparators 7032, and the like. The analog crosspoint switch 7022 may facilitate connecting one or more inputs 7024 to one or more outputs 7028 by interpreting a switch control value that may be provided to it by a controller 7034 and the like.

[0513] An example system for data collection in an industrial environment comprising includes analog signal sources that each connect to at least one input of an analog crosspoint switch including a plurality of inputs and a plurality of outputs; where the analog crosspoint switch is configurable to switch a portion of the input signal sources to a plurality of the outputs.

[0514] 2. In certain embodiments, the analog crosspoint switch further includes an analog-to-digital converter that converts a portion of analog signals input to the crosspoint switch into representative digital signals; a portion of the outputs including analog outputs and a portion of the outputs comprises digital outputs; and/or where the analog crosspoint switch is adapted to detect one or more analog input signal conditions. Any one or more of the example embodiments include the analog input signal conditions including a voltage range of the signal, and where the analog crosspoint switch responsively adjusts input circuitry to comply with detected voltage range. An example system of data collection in an industrial environment includes a number of industrial sensors that produce analog signals representative of a condition of an industrial machine in the environment being sensed by the number of industrial sensors, a crosspoint switch that receives the analog signals and routes the analog signals to separate analog outputs of the crosspoint switch based on a signal route plan presented to the crosspoint switch. In certain embodiments, the analog crosspoint switch further includes an analog-to-digital converter that converts a portion of analog signals input to the crosspoint switch into representative digital signals; where a portion of the outputs include analog outputs and a portion of the outputs include digital outputs; where the analog crosspoint switch is adapted to detect one or more analog input signal conditions; where the one or more analog input signal conditions include a voltage range of the signal, and/or where the analog crosspoint switch responsively adjusts input circuitry to comply with detected voltage range.

[0515] An example method of data collection in an industrial environment includes routing a number of analog signals along a plurality of analog signal paths by connecting the plurality of analog signals individually to inputs of an analog crosspoint switch, configuring the analog crosspoint switch with data routing information from a data collection template for the industrial environment routing, and routing with the configured analog crosspoint switch a portion of the number of analog signals to a portion the plurality of analog signal paths. In certain further embodiments, at least one output of the analog crosspoint switch includes a high current driver circuit; at least one input of the analog crosspoint switch includes a voltage limiting circuit; and/or at least one input of the analog crosspoint switch includes a current limiting circuit. In certain further embodiments, the analog crosspoint switch includes a number of interconnected relays that facilitate connecting any of a number of inputs to any of a plurality of outputs; the analog crosspoint switch further including an analog-to-digital converter that converts a portion of analog signals input to the crosspoint switch into a representative digital signal; the analog crosspoint switch further including signal processing functionality to detect one or more analog input signal conditions, and in response thereto, to perform an action (e.g., set an alarm, change switch configuration, disable one or more outputs, power off a portion of the switch, change a state of a general purpose (digital/analog) output, etc.); where a portion of the outputs are analog outputs and a portion of the outputs are digital outputs; where the analog crosspoint switch is adapted to detect one or more analog input signal conditions; where the analog crosspoint switch is adapted to take one or more actions in response to detecting the one or more analog input signal conditions, the one more actions including setting an alarm, sending an alarm signal, changing a configuration of the analog crosspoint switch, disabling an output, powering off a portion of the analog crosspoint switch, powering on a portion of the analog crosspoint switch, and/or controlling a general purpose output of the analog crosspoint switch.

[0516] An example system includes a power roller of a conveyor, including any of the described operations of an analog crosspoint switch. Without limitation, further example embodiments includes sensing conditions of the power roller by the sensors to determine a rate of rotation of the power roller, a load being transported by the power roller, power being consumed by the power roller, and/or a rate of acceleration of the power roller. An example system includes a fan in a factory setting, including any of the described operations of an analog crosspoint switch. Without limitation, certain further embodiments include sensors disposed to sense conditions of the fan, including a fan blade tip speed, torque, back pressure, RPMs, and/or a volume of air per unit time displaced by the fan. An example system includes a turbine in a power generation environment, including any of the described operations of an analog crosspoint switch. Without limitation, certain further embodiments include a number of sensors disposed to sense conditions of the turbine, where the sensed conditions include a relative shaft vibration, an absolute vibration of bearings, a turbine cover vibration, a thrust bearing axial vibration, vibrations of stators or stator cores, vibrations of stator bars, and/or vibrations of stator end windings.

[0517] In embodiments, methods and systems of data collection in an industrial environment may include a plurality of industrial condition sensing and acquisition modules that may include at least one programmable logic component per module that may control a portion of the sensing and acquisition functionality of its module. The programmable logic components on each of the modules may be interconnected by a dedicated logic bus that may include data and control channels. The dedicated logic bus may extend logically and/or physically to other programmable logic components on other sensing and acquisition modules. In embodiments, the programmable logic components may be programmed via the dedicated interconnection bus, via a dedicated programming portion of the dedicated interconnection bus, via a program that is passed between programmable logic components, sensing and acquisition modules, or whole systems. A programmable logic component for use in an industrial environment data sensing and acquisition system may be a Complex Programmable Logic Device, an Application-Specific Integrated Circuit, microcontrollers, and combinations thereof.

[0518] A programmable logic component in an industrial data collection environment may perform control functions associated with data collection. Control examples include power control of analog channels, sensors, analog receivers, analog switches, portions of logic modules (e.g., a logic board, system, and the like) on which the programmable logic component is disposed, self-power-up/down, self-sleep/wake up, and the like. Control functions, such as these and others, may be performed in coordination with control and operational functions of other programmable logic components, such as other components on a single data collection module and components on other such modules. Other functions that a programmable logic component may provide may include generation of a voltage reference, such as a precise voltage reference for input signal condition detection. A programmable logic component may generate, set, reset, adjust, calibrate, or otherwise determine the voltage of the reference, its tolerance, and the like. Other functions of a programmable logic component may include enabling a digital phase lock loop to facilitate tracking slowly transitioning input signals, and further to facilitate detecting the phase of such signals. Relative phase detection may also be implemented, including phase relative to trigger signals, other analog inputs, on-board references (e.g., on-board timers), and the like. A programmable logic component may be programmed to perform input signal peak voltage detection and control input signal circuitry, such as to implement auto-scaling of the input to an operating voltage range of the input. Other functions that may be programmed into a programmable logic component may include determining an appropriate sampling frequency for sampling inputs independently of their operating frequencies. A programmable logic component may be programmed to detect a maximum frequency among a plurality of input signals and set a sampling frequency for each of the input signals that is greater than the detected maximum frequency.

[0519] A programmable logic component may be programmed to configure and control data routing components, such as multiplexers, crosspoint switches, analog-to-digital converters, and the like, to implement a data collection template for the industrial environment. A data collection template may be included in a program for a programmable logic component. Alternatively, an algorithm that interprets a data collection template to configure and control data routing resources in the industrial environment may be included in the program.

[0520] In embodiments, one or more programmable logic components in an industrial environment may be programmed to perform smart-band signal analysis and testing. Results of such analysis and testing may include triggering smart band data collection actions, that may include reconfiguring one or more data routing resources in the industrial environment. A programmable logic component may be configured to perform a portion of smart band analysis, such as collection and validation of signal activity from one or more sensors that may be local to the programmable logic component. Smart band signal analysis results from a plurality of programmable logic components may be further processed by other programmable logic components, servers, machine learning systems, and the like to determine compliance with a smart band.

[0521] In embodiments, one or more programmable logic components in an industrial environment may be programmed to control data routing resources and sensors for outcomes, such as reducing power consumption (e.g., powering on/off resources as needed), implementing security in the industrial environment by managing user authentication, and the like. In embodiments, certain data routing resources, such as multiplexers and the like, may be configured to support certain input signal types. A programmable logic component may configure the resources based on the type of signals to be routed to the resources. In embodiments, the programmable logic component may facilitate coordination of sensor and data routing resource signal type matching by indicating to a configurable sensor a protocol or signal type to present to the routing resource. A programmable logic component may facilitate detecting a protocol of a signal being input to a data routing resource, such as an analog crosspoint switch and the like. Based on the detected protocol, the programmable logic component may configure routing resources to facilitate support and efficient processing of the protocol. In an example, a programmable logic component configured data collection module in an industrial environment may implement an intelligent sensor interface specification, such as IEEE 1451.2 intelligent sensor interface specification.

[0522] In embodiments, distributing programmable logic components across a plurality of data sensing, collection, and/or routing modules in an industrial environment may facilitate greater functionality and local inter-operational control. In an example, modules may perform operational functions independently based on a program installed in one or more programmable logic components associated with each module. Two modules may be constructed with substantially identical physical components, but may perform different functions in the industrial environment based on the program(s) loaded into programmable logic component(s) on the modules. In this way, even if one module were to experience a fault, or be powered down, other modules may continue to perform their functions due at least in part to each having its own programmable logic component(s). In embodiments, configuring a plurality of programmable logic components distributed across a plurality of data collection modules in an industrial environment may facilitate scalability in terms of conditions in the environment that may be sensed, the number of data routing options for routing sensed data throughout the industrial environment, the types of conditions that may be sensed, the computing capability in the environment, and the like.

[0523] In embodiments, a programmable logic controller-configured data collection and routing system may facilitate validation of external systems for use as storage nodes, such as for a distributed ledger, and the like. A programmable logic component may be programmed to perform validation of a protocol for communicating with such an external system, such as an external storage node.

[0524] In embodiments, programming of programmable logic components, such as CPLDs and the like may be performed to accommodate a range of data sensing, collection and configuration differences. In embodiments, reprogramming may be performed on one or more components when adding and/or removing sensors, when changing sensor types, when changing sensor configurations or settings, when changing data storage configurations, when embedding data collection template(s) into device programs, when adding and/or removing data collection modules (e.g., scaling a system), when a lower cost device is used that may limit functionality or resources over a higher cost device, and the like. A programmable logic component may be programmed to propagate programs for other programmable components via a dedicated programmable logic device programming channel, via a daisy chain programming architecture, via a mesh of programmable logic components, via a hub-and-spoke architecture of interconnected components, via a ring configuration (e.g., using a communication token, and the like).

[0525] In embodiments, a system for data collection in an industrial environment comprising distributed programmable logic devices connected by a dedicated control bus may be deployed with drilling machines in an oil and gas harvesting environment, such as an oil and/or gas field. A drilling machine has many active portions that may be operated, monitored, and adjusted during a drilling operation. Sensors to monitor a crown block may be physically isolated from sensors for monitoring a blowout preventer and the like. To effectively maintain control of this wide range and diverse disposition of sensors, programmable logic components, such as Complex Programmable Logic Devices (“CPLD”) may be distributed throughout the drilling machine. While each CPLD may be configured with a program to facilitate operation of a limited set of sensors, at least portions of the CPLD may be connected by a dedicated bus for facilitating coordination of sensor control, operation and use. In an example, a set of sensors may be disposed proximal to a mud pump or the like to monitor flow, density, mud tank levels, and the like. One or more CPLD may be deployed with each sensor (or a group of sensors) to operate the sensors and sensor signal routing and collection resources. The CPLD in this mud pump group may be interconnected by a dedicated control bus to facilitate coordination of sensor and data collection resource control and the like. This dedicated bus may extend physically and/or logically beyond the mud pump control portion of the drill machine so that CPLD of other portions (e.g., the crown block and the like) may coordinate data collection and related activity through portions of the drilling machine.

[0526] In embodiments, a system for data collection in an industrial environment comprising distributed programmable logic devices connected by a dedicated control bus may be deployed with compressors in an oil and gas harvesting environment, such as an oil and/or gas field. Compressors are used in the oil and gas industry for compressing a variety of gases and purposes include flash gas, gas lift, reinjection, boosting, vapor-recovery, casing head and the like. Collecting data from sensors for these different compressor functions may require substantively different control regimes. Distributing CPLDs programmed with different control regimes is an approach that may accommodate these diverse data collection requirements. One or more CPLDs may be disposed with sets of sensors for the different compressor functions. A dedicated control bus may be used to facilitate coordination of control and/or programming of CPLDs in and across compressor instances. In an example, a CPLD may be configured to manage a data collection infrastructure for sensors disposed to collect compressor-related conditions for flash gas compression; a second CPLD or group of CPLDs may be configured to manage a data collection infrastructure for sensors disposed to collect compressor related conditions for vapor-recovery gas compression. These groups of CPLDs may operate control programs.

[0527] In embodiments, a system for data collection in an industrial environment comprising distributed programmable logic devices connected by a dedicated control bus may be deployed in a refinery with turbines for oil and gas production, such as with modular impulse steam turbines. A system for collection of data from impulse steam turbines may be configured with a plurality of condition sensing and collection modules adapted for specific functions of an impulse steam turbine. Distributing CPLDs along with these modules can facilitate adaptable data collection to suit individual installations. As an example, blade conditions, such as tip rotational rate, temperature rise of the blades, impulse pressure, blade acceleration rate, and the like may be captured in data collection modules configured with sensors for sensing these conditions. Other modules may be configured to collect data associated with valves (e.g., in a multi-valve configuration, one or more modules may be configured for each valve or for a set of valves), turbine exhaust (e.g., radial exhaust data collection may be configured differently than axial exhaust data collection), turbine speed sensing may be configured differently for fixed versus variable speed implementations, and the like. Additionally, impulse gas turbine systems may be installed with other systems, such as combined cycle systems, cogeneration systems, solar power generation systems, wind power generation systems, hydropower generation systems, and the like. Data collection requirements for these installations may also vary. Using a CPLD-based, modular data collection system that uses a dedicated interconnection bus for the CPLDs may facilitate programming and/or reprogramming of each module directly in place without having to shut down or physically access each module.

[0528] Referring to FIG. 45, an exemplary embodiment of a system for data collection in an industrial environment comprising distributed CPLDs interconnected by a bus for control and/or programming thereof is depicted. An exemplary data collection module 7200 may comprise one or more CPLDs 7206 for controlling one or more data collection system resources, such as sensors 7202 and the like. Other data collection resources that a CPLD may control may include crosspoint switches, multiplexers, data converters, and the like. CPLDs on a module may be interconnected by a bus, such as a dedicated logic bus 7204 that may extend beyond a data collection module to CPLDs on other data collection modules. Data collection modules, such as module 7200 may be configured in the environment, such as on an industrial machine 7208 (e.g., an impulse gas turbine) and/or 7210 (e.g., a co-generation system), and the like. Control and/or configuration of the CPLDs may be handled by a controller 7212 in the environment. Data collection and routing resources and interconnection (not shown) may also be configured within and among data collection modules 7200 as well as between and among industrial machines 7208 and 7210, and/or with external systems, such as Internet portals, data analysis servers, and the like to facilitate data collection, routing, storage, analysis, and the like.

[0529] An example system for data collection in an industrial environment includes a number of industrial condition sensing and acquisition modules, with a programmable logic component disposed on each of the modules, where the programmable logic component controls a portion of the sensing and acquisition functional of the corresponding module. The system includes communication bus that is dedicated to interconnecting the at least one programmable logic component disposed on at least one of the plurality of modules, wherein the communication bus extends to other programmable logic components on other sensing and acquisition modules.

[0530] In certain further embodiments, a system includes the programmable logic component programmed via the communication bus, the communication bus including a portion dedicated to programming of the programmable logic components, controlling a portion of the sensing and acquisition functionality of a module by a power control function such as: controlling power of a sensor, a multiplexer, a portion of the module, and/or controlling a sleep mode of the programmable logic component; controlling a portion of the sensing and acquisition functionality of a module by providing a voltage reference to a sensor and/or an analog-to-digital converter disposed on the module, by detecting a relative the phase of at least two analog signals derived from at least two sensors disposed on the module; by controlling sampling of data provided by at least one sensor disposed on the module; by detecting a peak voltage of a signal provided by a sensor disposed on the module; and/or by configuring at least one multiplexer disposed on the module by specifying to the multiplexer a mapping of at least one input and one output. In certain embodiments, the communication bus extends to other programmable logic components on other condition sensing and/or acquisition modules. In certain embodiments, a module may be an industrial environment condition sensing module. In certain embodiments, a module control program includes an algorithm for implementing an intelligent sensor interface communication protocol, such as an IEEE1451.2 compatible intelligent sensor interface communication protocol. In certain embodiments, a programmable logic component includes configuring the programmable logic component and/or the sensing or acquisition module to implement a smart band data collection template. Example and non-limiting programmable logic components include field programmable gate arrays, complex programmable logic devices, and/or microcontrollers.

[0531] An example system includes a drilling machine for oil and gas field use, with a condition sensing and/or acquisition module to monitor aspects of a drilling machine. Without limitation, a further example system includes monitoring a compressor and/or monitoring an impulse steam engine.

[0532] In embodiments, a system for data collection in an industrial environment may include a trigger signal and at least one data signal that share a common output of a signal multiplexer and upon detection of a condition in the industrial environment, such as a state of the trigger signal, the common output is switched to propagate either the data signal or the trigger signal. Sharing an output between a data signal and a trigger signal may also facilitate reducing a number of individually routed signals in an industrial environment. Benefits of reducing individually routed signals may include reducing the number of interconnections between data collection module, thereby reducing the complexity of the industrial environment. Trade-offs for reducing individually routed signals may include increasing sophistication of logic at signal switching modules to implement the detection and conditional switching of signals. A net benefit of this added localized logic complexity may be an overall reduction in the implementation complexity of such a data collection system in an industrial environment.

[0533] Exemplary deployment environments may include environments with trigger signal channel limitations, such as existing data collection systems that do not have separate trigger support for transporting an additional trigger signal to a module with sufficient computing sophistication to perform trigger detection. Another exemplary deployment may include systems that require at least some autonomous control for performing data collection.

[0534] In embodiments, a system for data collection in an industrial environment may include an analog switch that switches between a first input, such as a trigger input and a second input, such as a data input based on a condition of the first input. A trigger input may be monitored by a portion of the analog switch to detect a change in the signal, such as from a lower voltage to a higher voltage relative to a reference or trigger threshold voltage. In embodiments, a device that may receive the switched signal from the analog switch may monitor the trigger signal for a condition that indicates a condition for switching from the trigger input to the data input. When a condition of the trigger input is detected, the analog switch may be reconfigured, to direct the data input to the same output that was propagating the trigger output.

[0535] In embodiments, a system for data collection in an industrial environment may include an analog switch that directs a first input to an output of the analog switch until such time as the output of the analog switch indicates that a second input should be directed to the output of the analog switch. The output of the analog switch may propagate a trigger signal to the output. In response to the trigger signal propagating through the switch transitioning from a first condition (e.g., a first voltage below a trigger threshold voltage value) to a second condition (e.g., a second voltage above the trigger threshold voltage value), the switch may stop propagating the trigger signal and instead propagate another input signal to the output. In embodiments, the trigger signal and the other data signal may be related, such as the trigger signal may indicate a presence of an object being placed on a conveyer and the data signal represents a strain placed on the conveyer.

[0536] In embodiments, to facilitate timely detection of the trigger condition, a rate of sampling of the output of the analog switch may be adjustable, so that, for example, the rate of sampling is higher while the trigger signal is propagated and lower when the data signal is propagated. Alternatively, a rate of sampling may be fixed for either the trigger or the data signal. In embodiments, the rate of sampling may be based on a predefined time from trigger occurrence to trigger detection and may be faster than a minimum sample rate to capture the data signal.

[0537] In embodiments, routing a plurality of hierarchically organized triggers onto another analog channel may facilitate implementing a hierarchical data collection triggering structure in an industrial environment. A data collection template to implement a hierarchical trigger signal architecture may include signal switch configuration and function data that may facilitate a signal switch facility, such as an analog crosspoint switch or multiplexer to output a first input trigger in a hierarchy, and based on the first trigger condition being detected, output a second input trigger in the hierarchy on the same output as the first input trigger by changing an internal mapping of inputs to outputs. Upon detection of the second input trigger condition, the output may be switched to a data signal, such as data from a sensor in an industrial environment.

[0538] In embodiments, upon detection of a trigger condition, in addition to switching from the trigger signal to a data signal, an alarm may be generated and optionally propagated to a higher functioning device/module. In addition to switching to a data signal, upon detection of a state of the trigger, sensors that otherwise may be disabled or powered down may be energized/activated to begin to produce data for the newly selected data signal. Activating might alternatively include sending a reset or refresh signal to the sensor(s).

[0539] In embodiments, a system for data collection in an industrial environment may include a system for routing a trigger signal onto a data signal path in association with a gearbox of an industrial vehicle. Combining a trigger signal onto a signal path that is also used for a data signal may be useful in gearbox applications by reducing the number of signal lines that need to be routed, while enabling advanced functions, such as data collection based on pressure changes in the hydraulic fluid and the like. As an example, a sensor may be configured to detect a pressure difference in the hydraulic fluid that exceeds a certain threshold as may occur when the hydraulic fluid flow is directed back into the impeller to give higher torque at low speeds. The output of such a sensor may be configured as a trigger for collecting data about the gearbox when operating at low speeds. In an example, a data collection system for an industrial environment may have a multiplexer or switch that facilitates routing either a trigger or a data channel over a single signal path. Detecting the trigger signal from the pressure sensor may result in a different signal being routed through the same line that the trigger signal was routed by switching a set of controls. A multiplexer may, for example, output the trigger signal until the trigger signal is detected as indicating that the output should be changed to the data signal. As a result of detecting the high-pressure condition, a data collection activity may be activated so that data can be collected using the same line that was recently used by the trigger signal.

[0540] In embodiments, a system for data collection in an industrial environment may include a system for routing a trigger signal onto a data signal path in association with a vehicle suspension for truck and car operation. Vehicle suspension, particularly active suspension may include sensors for detecting road events, suspension conditions, and vehicle data, such as speed, steering, and the like. These conditions may not always need to be detected, except, for example, upon detection of a trigger condition. Therefore, combining the trigger condition signal and at least one data signal on a single physical signal routing path could be implemented. Doing so may reduce costs due to fewer physical connections required in such a data collection system. In an example, a sensor may be configured to detect a condition, such as a pot hole, to which the suspension must react. Data from the suspension may be routed along the same signal routing path as this road condition trigger signal so that upon detection of the pot hole, data may be collected that may facilitate determining aspects of the suspension's reaction to the pot hole.

[0541] In embodiments, a system for data collection in an industrial environment may include a system for routing a trigger signal onto a data signal path in association with a turbine for power generation in a power station. A turbine used for power generation may be retrofitted with a data collection system that optimizes existing data signal lines to implement greater data collection functions. One such approach involves routing new sources of data over existing lines. While multiplexing signals generally satisfies this need, combining a trigger signal with a data signal via a multiplexer or the like can further improve data collection. In an example, a first sensor may include a thermal threshold sensor that may measure the temperature of an aspect of a power generation turbine. Upon detection of that trigger (e.g., by the temperature rising above the thermal threshold), a data collection system controller may send a different data collection signal over the same line that was used to detect the trigger condition. This may be accomplished by a controller or the like sensing the trigger signal change condition and then signaling to the multiplexer to switch from the trigger signal to a data signal to be output on the same line as the trigger signal for data collection. In this example, when a turbine is detected as having a portion that exceeds its safe thermal threshold, a secondary safety signal may be routed over the trigger signal path and monitored for additional safety conditions, such as overheating and the like.

[0542] Referring to FIG. 46, an embodiment of routing a trigger signal over a data signal path in a data collection system in an industrial environment is depicted. Signal multiplexer 7400 may receive a trigger signal on a first input from a sensor or other trigger source 7404 and a data signal on a second input from a sensor for detecting a temperature associated with an industrial machine in the environment 7402. The multiplexer 7400 may be configured to output the trigger signal onto an output signal path 7406. A data collection module 7410 may process the signal on the data path 7406 looking for a change in the signal indicative of a trigger condition provided from the trigger sensor 7404 through the multiplexer 7400. Upon detection, a control output 7408 may be changed and thereby control the multiplexer 7400 to start outputting data from the temperature probe 7402 by switching an internal switch or the like that may control one or more of the inputs that may be routed to the output 7406. Data collection facility 7410 may activate a data collection template in response to the detected trigger that may include switching the multiplexer and collecting data into triggered data storage 7412. Upon completion of the data collection activity, multiplexer control signal 7408 may revert to its initial condition so that trigger sensor 7404 may be monitored again.

[0543] An example system for data collection in an industrial environment includes an analog switch that directs a first input to an output of the analog switch until such time as the output of the analog switch indicates that a second input should be directed to the output of the analog switch. In certain further embodiments, the example system includes: where the output of the analog switch indicated that the second input should be directed to the output based on the output transitioning from a pending condition to a triggered condition; wherein the triggered condition includes detecting the output presenting a voltage above a trigger voltage value; routing a number of signals with the analog switch from inputs on the analog switch to outputs on the analog switch in response to the output of the analog switch indicating that the second input should be directed to the output; sampling the output of the analog switch at a rate that exceeds a rate of transition for a number of signals input to the analog switch; and/or generating an alarm signal when the output of the analog switch indicates that a second input should be directed to the output of the analog switch.

[0544] An example system for data collection in an industrial environment includes an analog switch that switches between a first input and a second input based on a condition of the first input. In certain further embodiments, the condition of the first input comprises the first input presenting a triggered condition, and/or the triggered condition includes detecting the first input presenting a voltage above a trigger voltage value. In certain embodiments, the analog switch includes routing a plurality of signals with the analog from inputs on the analog switch to outputs on the analog switch based on the condition of the first input, sampling an input of the analog switch at a rate that exceeds a rate of transition for a plurality of signals input to the analog switch, and/or generating an alarm signal based on the condition of the first input.

[0545] An example system for data collection in an industrial environment includes a trigger signal and at least one data signal that share a common output of a signal multiplexer, and upon detection of a predefined state of the trigger signal, the common output is configured to propagate the at least one data signal through the signal multiplexer. In certain further embodiments, the signal multiplexer is an analog multiplexer, the predefined state of the trigger signal is detected on the common output, detection of the predefined state of the trigger signal includes detecting the common output presenting a voltage above a trigger voltage value, the multiplexer includes routing a plurality of signals with the multiplexer from inputs on the multiplexer to outputs on the multiplexer in response to detection of the predefined state of the trigger signal, the multiplexer includes sampling the output of the multiplexer at a rate that exceeds a rate of transition for a plurality of signals input to the multiplexer, the multiplexer includes generating an alarm in response to detection of the predefined state of the trigger signal, and/or the multiplexer includes activating at least one sensor to produce the at least one data signal. Without limitation, example systems include: monitoring a gearbox of an industrial vehicle by directing a trigger signal representing a condition of the gearbox to an output of the analog switch until such time as the output of the analog switch indicates that a second input representing a condition of the gearbox related to the trigger signal should be directed to the output of the analog switch; monitoring a suspension system of an industrial vehicle by directing a trigger signal representing a condition of the suspension to an output of the analog switch until such time as the output of the analog switch indicates that a second input representing a condition of the suspension related to the trigger signal should be directed to the output of the analog switch; and/or monitoring a power generation turbine by directing a trigger signal representing a condition of the power generation turbine to an output of the analog switch until such time as the output of the analog switch indicates that a second input representing a condition of the power generation turbine related to the trigger signal should be directed to the output of the analog switch.

[0546] In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors at least one signal for a set of collection band parameters and upon detection of a parameter from the set of collection band parameters in the signal, configures collection of data from a set of sensors based on the detected parameter. The set of selected sensors, the signal, and the set of collection band parameters may be part of a smart bands data collection template that may be used by the system when collecting data in an industrial environment. A motivation for preparing a smart-bands data collection template may include monitoring a set of conditions of an industrial machine to facilitate improved operation, reduce down time, preventive maintenance, failure prevention, and the like. Based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken, such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventive maintenance, triggering data collection from additional sets of sensors, and the like. An example of data that may indicate a need for some action may include changes that may be detectable through trends present in the data from the set of sensors. Another example is trends of analysis values derived from the set of sensors.

[0547] In embodiments, the set of collection band parameters may include values received from a sensor that is configured to sense a condition of the industrial machine (e.g., bearing vibration). However, a set of collection band parameters may instead be a trend of data received from the sensor (e.g., a trend of bearing vibration across a plurality of vibration measurements by a bearing vibration sensor). In embodiments, a set of collection band parameters may be a composite of data and/or trends of data from a plurality of sensors (e.g., a trend of data from on-axis and off-axis vibration sensors). In embodiments, when a data value derived from one or more sensors as described herein is sufficiently close to a value of data in the set of collection band parameters, the data collection activity from the set of sensors may be triggered. Alternatively, a data collection activity from the set of sensors may be triggered when a data value derived from the one or more sensors (e.g., trends and the like) falls outside of a set of collection band parameters. In an example, a set of data collection band parameters for a motor may be a range of rotational speeds from 95% to 105% of a select operational rotational speed. So long as a trend of rotational speed of the motor stays within this range, a data collection activity may be deferred. However, when the trend reaches or exceeds this range, then a data collection activity, such as one defined by a smart bands data collection template may be triggered.

[0548] In embodiments, triggering a data collection activity, such as one defined by a smart bands data collection template, may result in a change to a data collection system for an industrial environment that may impact aspects of the system such as data sensing, switching, routing, storage allocation, storage configuration, and the like. This change to the data collection system may occur in near real time to the detection of the condition; however, it may be scheduled to occur in the future. It may also be coordinated with other data collection activities so that active data collection activities, such as a data collection activity for a different smart bands data collection template, can complete prior to the system being reconfigured to meet the smart bands data collection template that is triggered by the sensed condition meeting the smart bands data collection trigger.

[0549] In embodiments, processing of data from sensors may be cumulative over time, over a set of sensors, across machines in an industrial environment, and the like. While a sensed value of a condition may be sufficient to trigger a smart bands data collection template activity, data may need to be collected and processed over time from a plurality of sensors to generate a data value that may be compared to a set of data collection band parameters for conditionally triggering the data collection activity. Using data from multiple sensors and/or processing data, such as to generate a trend of data values and the like may facilitate preventing inconsequential instances of a sensed data value being outside of an acceptable range from causing unwarranted smart bands data collection activity. In an example, if a vibration from a bearing is detected outside of an acceptable range infrequently, then trending for this value over time may be useful to detect if the frequency is increasing, decreasing, or staying substantially constant or within a range of values. If the frequency of such a value is found to be increasing, then such a trend is indicative of changes occurring in operation of the industrial machine as experienced by the bearing. An acceptable range of values of this trended vibration value may be established as a set of data collection band parameters against which vibration data for the bearing will be monitored. When the trended vibration value is outside of this range of acceptable values, a smart bands data collection activity may be activated.

[0550] In embodiments, a system for data collection in an industrial environment that supports smart band data collection templates may be configured with data processing capability at a point of sensing of one or more conditions that may trigger a smart bands data collection template data collection activity, such as: by use of an intelligent sensor that may include data processing capabilities; by use of a programmable logic component that interfaces with a sensor and processes data from the sensor; by use of a computer processor, such as a microprocessor and the like disposed proximal to the sensor; and the like. In embodiments, processing of data collected from one or more sensors for detecting a smart bands template data collection activity may be performed by remote processors, servers, and the like that may have access to data from a plurality of sensors, sensor modules, industrial machines, industrial environments, and the like.

[0551] In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors an industrial environment for a set of parameters, and upon detection of at least one parameter, configures the collection of data from a set of sensors and causes a data storage controller to adapt a configuration of data storage facilities to support collection of data from the set of sensors based on the detected parameter. The methods and systems described herein for conditionally changing a configuration of a data collection system in an industrial environment to implement a smart bands data collection template may further include changes to data storage architectures. As an example, a data storage facility may be disposed on a data collection module that may include one or more sensors for monitoring conditions in an industrial environment. This local data storage facility may typically be configured for rapid movement of sensed data from the module to a next level sensing or processing module or server. When a smart bands data collection condition is detected, sensor data from a plurality of sensors may need to be captured concurrently. To accommodate this concurrent collection, the local memory may be reconfigured to capture data from each of the plurality of sensors in a coordinated manner, such as repeatedly sampling each of the sensors synchronously, or with a known offset, and the like, to build up a set of sensed data that may be much larger than would typically be captured and moved through the local memory. A storage control facility for controlling the local storage may monitor the movement of sensor data into and out of the local data storage, thereby ensuring safe movement of data from the plurality of sensors to the local data storage and on to a destination, such as a server, networked storage facility, and the like. The local data storage facility may be configured so that data from the set of sensors associated with a smart bands data collection template are securely stored and readily accessible as a set of smart band data to facilitate processing the smart band-specific data. As an example, local storage may comprise non-volatile memory (NVM). To prepare for data collection in response to a smart band data collection template being triggered, portions of the NVM may be erased to prepare the NVM to receive data as indicated in the template.

[0552] In embodiments, multiple sensors may be arranged into a set of sensors for condition-specific monitoring. Each set, which may be a logical set of sensors, may be selected to provide information about elements in an industrial environment that may provide insight into potential problems, root causes of problems, and the like. Each set may be associated with a condition that may be monitored for compliance with an acceptable range of values. The set of sensors may be based on a machine architecture, hierarchy of components, or a hierarchy of data that contributes to a finding about a machine that may usefully be applied to maintaining or improving performance in the industrial environment. Smart band sensor sets may be configured based on expert system analysis of complex conditions, such as machine failures and the like. Smart band sensor sets may be arranged to facilitate knowledge gathering independent of a particular failure mode or history. Smart band sensor sets may be arranged to test a suggested smart band data collection template prior to implementing it as part of an industrial machine operations program. Gathering and processing data from sets of sensors may facilitate determining which sensors contribute meaningful data to the set, and those sensors that do not contribute can be removed from the set. Smart band sensor sets may be adjusted based on external data, such as industry studies that indicate the types of sensor data that is most helpful to reduce failures in an industrial environment.

[0553] In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors at least one signal for compliance to a set of collection band conditions and upon detection of a lack of compliance, configures the collection of data from a predetermined set of sensors associated with the monitored signal. Upon detection of a lack of compliance, a collection band template associated with the monitored signal may be accessed, and resources identified in the template may be configured to perform the data collection. In embodiments, the template may identify sensors to activate, data from the sensors to collect, duration of collection or quantity of data to be collected, destination (e.g., memory structure) to store the collected data, and the like. In embodiments, a smart band method for data collection in an industrial environment may include periodic collection of data from one or more sensors configured to sense a condition of an industrial machine in the environment. The collected data may be checked against a set of criteria that define an acceptable range of the condition. Upon validation that the collected data is either approaching one end of the acceptable limit or is beyond the acceptable range of the condition, data collection may commence from a smart-band group of sensors associated with the sensed condition based on a smart-band collection protocol configured as a data collection template. In embodiments, an acceptable range of the condition is based on a history of applied analytics of the condition. In embodiments, upon validation of the acceptable range being exceeded, data storage resources of a module in which the sensed condition is detected may be configured to facilitate capturing data from the smart band group of sensors.

[0554] In embodiments, monitoring a condition to trigger a smart band data collection template data collection action may be: in response to: a regulation, such as a safety regulation; in response to an upcoming activity, such as a portion of the industrial environment being shut down for preventive maintenance; in response to sensor data missing from routine data collection activities; and the like. In embodiments, in response to a faulty sensor or sensor data missing from a smart band template data collection activity, one or more alternate sensors may be temporarily included in the set of sensors so as to provide data that may effectively substitute for the missing data in data processing algorithms.

[0555] In embodiments, smart band data collection templates may be configured for detecting and gathering data for smart band analysis covering vibration spectra, such as vibration envelope and current signature for spectral regions or peaks that may be combinations of absolute frequency or factors of machine related parameters, vibration time waveforms for time-domain derived calculations including, without limitation: RMS overall, peak overall, true peak, crest factor, and the like; vibration vectors, spectral energy humps in various regions (e.g., low-frequency region, high frequency region, low orders, and the like); pressure-volume analysis and the like.

[0556] In embodiments, a system for data collection that applies smart band data collection templates may be applied to an industrial environment, such as ball screw actuators in an automated production environment. Smart band analysis may be applied to ball screw actuators in industrial environments such as precision manufacturing or positioning applications (e.g., semiconductor photolithography machines, and the like). As a typical primary objective of using a ball screw is for precise positioning, detection of variation in the positioning mechanism can help avoid costly defective production runs. Smart bands triggering and data collection may help in such applications by detecting, through smart band analysis, potential variations in the positioning mechanism such as in the ball screw mechanism, a worm drive, a linear motor, and the like. In an example, data related to a ball screw positioning system may be collected with a system for data collection in an industrial environment as described herein. A plurality of sensors may be configured to collect data such as screw torque, screw direction, screw speed, screw step, screw home detection, and the like. Some portion of this data may be processed by a smart bands data analysis facility to determine if variances, such as trends in screw speed as a function of torque, approach or exceed an acceptable threshold. Upon such a determination, a data collection template for the ball screw production system may be activated to configure the data sensing, routing, and collection resources of the data collection system to perform data collection to facilitate further analysis. The smart band data collection template facilitates rapid collection of data from other sensors than screw speed and torque, such as position, direction, acceleration, and the like by routing data from corresponding sensors over one or more signal paths to a data collector. The duration and order of collection of the data from these sources may be specified in the smart bands data collection template so that data required for further analysis is effectively captured.

[0557] In embodiments, a system for data collection that applies smart band data collection templates to configure and utilize data collection and routing infrastructure may be applied to ventilation systems in mining environments. Ventilation provides a crucial role in mining safety. Early detection of potential problems with ventilation equipment can be aided by applying a smart bands approach to data collection in such an environment. Sensors may be disposed for collecting information about ventilation operation, quality, and performance throughout a mining operation. At each ventilation device, ventilation-related elements, such as fans, motors, belts, filters, temperature gauges, voltage, current, air quality, poison detection, and the like may be configured with a corresponding sensor. While variation in any one element (e.g., air volume per minute, and the like) may not be indicative of a problem, smart band analysis may be applied to detect trends over time that may be suggestive of potential problems with ventilation equipment. To perform smart bands analysis, data from a plurality of sensors may be required to form a basis for analysis. By implementing data collection systems for ventilation stations, data from a ventilation system may be captured. In an example, a smart band analysis may be indicated for a ventilation station. In response to this indication, a data collection system may be configured to collect data by routing data from sensors disposed at the ventilation station to a central monitoring facility that may gather and analyze data from several ventilation stations.

[0558] In embodiments, a system for data collection that applies smart band data collection templates to configure and utilize data collection and routing infrastructure may be applied to drivetrain data collection and analysis in mining environments. A drivetrain, such as a drivetrain for a mining vehicle, may include a range of elements that could benefit from use of the methods and systems of data collection in an industrial environment as described herein. In particular, smart band-based data collection may be used to collect data from heavy duty mining vehicle drivetrains under certain conditions that may be detectable by smart bands analysis. A smart bands-based data collection template may be used by a drivetrain data collection and routing system to configure sensors, data paths, and data collection resources to perform data collection under certain circumstances, such as those that may indicate an unacceptable trend of drivetrain performance. A data collection system for an industrial drivetrain may include sensing aspects of a non-steering axle, a planetary steering axle, driveshafts, (e.g., main and wing shafts), transmissions, (e.g., standard, torque converters, long drop), and the like. A range of data related to these operational parts may be collected. However, data for support and structural members that support the drivetrain may also need to be collected for thorough smart band analysis. Therefore, collection across this wide range of drivetrain-related components may be triggered based on a smart band analysis determination of a need for this data. In an example, a smart band analysis may indicate potential slippage between a main and wing driveshaft that may represented by an increasing trend in response delay time of the wing drive shaft to main drive shaft operation. In response to this increasing trend, data collection modules disposed throughout the mining vehicle's drivetrain may be configured to route data from local sensors to be collected and analyzed by data collectors. Mining vehicle drivetrain smart based data collection may include a range of templates based on which type of trend is detected. If a trend related to a steering axle is detected, a data collection template to be implemented may be different in sensor content, duration, and the like than for a trend related to power demand for a normalized payload. Each template could configure data sensing, routing, and collection resources throughout the vehicle drivetrain accordingly.

[0559] Referring to FIG. 47, a system for data collection in an industrial environment that facilitates data collection for smart band analysis is depicted. A system for data collection in an industrial environment may include a smart band analysis data collection template repository 7600 in which smart band templates 7610 for data collection system configuration and collection of data may be stored and accessed by a data collection controller 7602. The templates 7610 may include data collection system configuration 7604 and operation information 7606 that may identify sensors, collectors, signal paths, and information for initiation and coordination of collection, and the like. A controller 7602 may receive an indication, such as a command from a smart band analysis facility 7608 to select and implement a specific smart band template 7610. The controller 7602 may access the template 7610 and configure the data collection system resources based on the information in that template. In embodiments, the template may identify: specific sensors; a multiplexer/switch configuration, data collection trigger/initiation signals and/or conditions, time duration and/or amount of data for collection; destination of collected data; intermediate processing, if any; and any other useful information, (e.g., instance identifier, and the like). The controller 7602 may configure and operate the data collection system to perform the collection for the smart band template and optionally return the system configuration to a previous configuration.

[0560] An example system for data collection in an industrial environment includes a data collection system that monitors at least one signal for a set of collection band parameters and, upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors based on the detected parameter. In certain further embodiments, the signal includes an output of a sensor that senses a condition in the industrial environment, where the set of collection band parameters comprises values derivable from the signal that are beyond an acceptable range of values derivable from the signal; where the at least one signal includes an output of a sensor that senses a condition in the industrial environment; wherein configuring portions of the system includes configuring a storage facility to accept data collected from the set of sensors; where configuring portions of the system includes configuring a data routing portion includes at least one of: an analog crosspoint switch, a hierarchical multiplexer, an analog-to-digital converter, an intelligent sensor, and/or a programmable logic component; wherein detection of a parameter from the set of collection band parameters comprises detecting a trend value for the signal being beyond an acceptable range of trend values; and/or where configuring portions of the system includes implementing a smart band data collection template associated with the detected parameter. In certain embodiments, a data collection system monitors a signal for data values within a set of acceptable data values that represent acceptable collection band conditions for the signal and, upon detection of a data value for the at least one signal outside of the set of acceptable data values, triggers a data collection activity that causes collecting data from a predetermined set of sensors associated with the monitored signal. In certain further embodiment, a data collection system includes the signal including an output of a sensor that senses a condition in the industrial environment; where the set of acceptable data value includes values derivable from the signal that are within an acceptable range of values derivable from the signal; configuring a storage facility of the system to facilitate collecting data from the predetermined set of sensors in response to the detection of a data value outside of the set of acceptable data values; configuring a data routing portion of the system including an analog crosspoint switch, a hierarchical multiplexer, an analog-to-digital converter, an intelligent sensor, and/or a programmable logic component in response to detecting a data value outside of the set of acceptable data values; where detection of a data value for the signal outside of the set of acceptable data values includes detecting a trend value for the signal being beyond an acceptable range of trend values; and/or where the data collection activity is defined by a smart band data collection template associated with the detected parameter.

[0561] An example method for data collection in an industrial environment comprising includes an operation to collect data from sensor(s) configured to sense a condition of an industrial machine in the environment; an operation to check the collected data against a set of criteria that define an acceptable range of the condition; and in response to the collected data violating the acceptable range of the condition, an operation to collect data from a smart-band group of sensors associated with the sensed condition based on a smart-band collection protocol configured as a smart band data collection template. In certain further embodiments, a method includes where violating the acceptable range of the condition includes a trend of the data from the sensor(s) approaching a maximum value of the acceptable range; where the smart-band group of sensors is defined by the smart band data collection template; where the smart band data collection template includes a list of sensors to activate, data from the sensors to collect, duration of collection of data from the sensors, and/or a destination location for storing the collected data; where collecting data from a smart-band group of sensors includes configuring at least one data routing resource of the industrial environment that facilitates routing data from the smart band group of sensors to a plurality of data collectors; and/or where the set of criteria includes a range of trend values derived by processing the data from sensor(s).

[0562] Without limitation, an example system monitors a ball screw actuator in an automated production environment, and monitors at least one signal from the ball screw actuator for a set of collection band parameters and, upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors disposed to monitor conditions of the ball screw actuator based on the detected parameter; another example system monitors a ventilation system in a mining environment, and monitors at least one signal from the ventilation system for a set of collection band parameters and, upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors disposed to monitor conditions of the ventilation system based on the detected parameter. An example system monitors a drivetrain of a mining vehicle, and monitors at least one signal from the drivetrain for a set of collection band parameters and, upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors disposed to monitor conditions of the drivetrain based on the detected parameter.

[0563] In embodiments, a system for data collection in an industrial environment may automatically configure local and remote data collection resources and may perform data collection from a plurality of system sensors that are identified as part of a group of sensors that produce data that is required to perform operational deflection shape rendering. In embodiments, the system sensors are distributed throughout structural portions of an industrial machine in the industrial environment. In embodiments, the system sensors sense a range of system conditions including vibration, rotation, balance, friction, and the like. In embodiments, automatically configuring is in response to a condition in the environment being detected outside of an acceptable range of condition values. In embodiments, a sensor in the identified group of system sensors senses the condition.

[0564] In embodiments, a system for data collection in an industrial environment may configure a data collection plan, such as a template, to collect data from a plurality of system sensors distributed throughout a machine to facilitate automatically producing an operational deflection shape visualization (“ODSV”) based on machine structural information and a data set used to produce an ODSV of the machine.

[0565] In embodiments, a system for data collection in an industrial environment may configure a data collection template for collecting data in an industrial environment by identifying sensors disposed for sensing conditions of preselected structural members of an industrial machine in the environment based on an ODSV of the industrial machine. In embodiments, the template may include an order and timing of data collection from the identified sensors.

[0566] In embodiments, methods and systems for data collection in an industrial environment may include a method of establishing an acceptable range of sensor values for a plurality of industrial machine condition sensors by validating an operational deflection shape visualization of structural elements of the machine as exhibiting deflection within an acceptable range, wherein data from the plurality of sensors used in the validated ODSV define the acceptable range of sensor values.

[0567] In embodiments, a system for data collection in an industrial environment may include a plurality of data sources, such as sensors, that may be grouped for coordinated data collection to provide data required to produce an ODSV. Information regarding the sensors to group, data collection coordination requirements, and the like may be retrieved from an ODSV data collection template. Coordinated data collection may include concurrent data collection. To facilitate concurrent data collection from a portion of the group of sensors, sensor routing resources of the system for data collection may be configured, such as by configuring a data multiplexer to route data from the portion of the group of sensors to which it connects to data collectors. In embodiments, each such source that connects an input of the multiplexer may be routed within the multiplexer to separate outputs so that data from all of the connected sources may be routed on to data collection elements of the industrial environment. In embodiments, the multiplexer may include data storage capabilities that may facilitate sharing a common output for at least a portion of the inputs. In embodiments, a multiplexer may include data storage capabilities and data bus-enabled outputs so that data for each source may be captured in a memory and transmitted over a data bus, such as a data bus that is common to the outputs of the multiplexer. In embodiments, sensors may be smart sensors that may include data storage capabilities and may send data from the data storage to the multiplexer in a coordinated manner that supports use of a common output of the multiplexer and/or use of a common data bus.

[0568] In embodiments, a system for data collection in an industrial environment may comprise templates for configuring the data collection system to collect data from a plurality of sensors to perform ODSV for a plurality of deflection shapes. Individual templates may be configured for visualization of looseness, soft joints, bending, twisting, and the like. Individual deflection shape data collection templates may be configured for different portions of a machine in an industrial environment.

[0569] In embodiments, a system for data collection in an industrial environment may facilitate operational deflection shape visualization that may include visualization of locations of sensors that contributed data to the visualization. In the visualization, each sensor that contributed data to generate the visualization may be indicated by a visual element. The visual element may facilitate user access to information about the sensor, such as location, type, representative data contributed, path of data from the sensor to a data collector, a deflection shape template identifier, a configuration of a switch or multiplexer through which the data is routed, and the like. The visual element may be determined by associating sensor identification information received from a sensor with information, such as a sensor map, that correlates sensor identification information with physical location in the environment. The information may appear in the visualization in response to the visual element representing the sensor being selected, such as by a user positioning a cursor on the sensor visual element.

[0570] In embodiments, ODSV may benefit from data satisfying a phase relationship requirement. A data collection system in the environment may be configured to facilitate collecting data that complies with the phase relationship requirement. Alternatively, the data collection system may be configured to collect data from a plurality of sensors that contains data that satisfies the phase relationship requirements but may also include data that does not. A post processing operation that may access phase detection data may select a subset of the collected data.

[0571] In embodiments, a system for data collection in an industrial environment may include a multiplexer receiving data from a plurality of sensors and multiplexing the received data for delivery to a data collector. The data collector may process the data to facilitate ODSV. ODSV may require data from several different sensors, and may benefit from using a reference signal, such as data from a sensor, when processing data from the different sensors. The multiplexer may be configured to provide data from the different sensors, such as by switching among its inputs over time so that data from each sensor may be received by the data collector. However, the multiplexer may include a plurality of outputs so that at least a portion of the inputs may be routed to least two of the plurality of outputs. Therefore, in embodiments, a multiple output multiplexer may be configured to facilitate data collection that may be suitable for ODSV by routing a reference signal from one of its inputs (e.g., data from an accelerometer) to one of its outputs and multiplexing data from a plurality of its outputs onto one or more of its outputs while maintaining the reference signal output routing. A data collector may collect the data from the reference output and use that to align the multiplexed data from the other sensors.

[0572] In embodiments, a system for data collection in an industrial environment may facilitate ODSV through coordinated data collection related to conveyors for mining applications. Mining operations may rely on conveyor systems to move material, supplies, and equipment into and out of a mine. Mining operations may typically operate around the clock; therefore, conveyor downtime may have a substantive impact on productivity and costs. Advanced analysis of conveyor and related systems that focuses on secondary affects that may be challenging to detect merely through point observation may be more readily detected via ODSV. Capturing operational data related to vibration, stresses, and the like can facilitate ODSV. However, coordination of data capture provides more reliable results. Therefore, a data collection system that may have sensors dispersed throughout a conveyor system can be configured to facilitate such coordinated data collection. In an example, capture of data affecting structural components of a conveyor, such as; landing points and the horizontal members that connect them and support the conveyer between landing points; conveyer segment handoff points; motor mounts; mounts of conveyer rollers and the like may need to be coordinated with data related to conveyor dynamic loading, drive systems, motors, gates, and the like. A system for data collection in an industrial environment, such as a mining environment may include data sensing and collection modules placed throughout the conveyor at locations such as segment handoff points, drive systems, and the like. Each module may be configured by one or more controllers, such as programmable logic controllers, that may be connected through a physical or logical (e.g., wireless) communication bus that aids in performing coordinated data collection. To facilitate coordination, a reference signal, such as a trigger and the like, may be communicated among the modules for use when collecting data. In embodiments, data collection and storage may be performed at each module so as to reduce the need for real-time transfer of sensed data throughout the mining environment. Transfer of data from the modules to an ODSV processing facility may be performed after collection, or as communication bandwidth between the modules and the processing facility allows. ODSV can provide insight into conditions in the conveyer, such as deflection of structural members that may, over time cause premature failure. Coordinated data collection with a data collection system for use in an industrial environment, such as mining, can enable ODSV that may reduce operating costs by reducing downtime due to unexpected component failure.

[0573] In embodiments, a system for data collection in an industrial environment may facilitate operational deflection shape visualization through coordinated data collection related to fans for mining applications. Fans provide a crucial function in mining operations of moving air throughout a mine to provide ventilation, equipment cooling, combustion exhaust evacuation, and the like. Ensuring reliable and often continuous operation of fans may be critical for miner safety and cost-effective operations. Dozens or hundreds of fans may be used in large mining operations. Fans, such as fans for ventilation management may include circuit, booster, and auxiliary types. High capacity auxiliary fans may operate at high speeds, over 2500 RPMs. Performing ODSV may reveal important reliability information about fans deployed in a mining environment. Collecting the range of data needed for ODSV of mining fans may be performed by a system for collecting data in industrial environments as described herein. In embodiments, sensing elements, such as intelligent sensing and data collection modules may be deployed with fans and/or fan subsystems. These modules may exchange collection control information (e.g., over a dedicated control bus and the like) so that data collection may be coordinated in time and phase to facilitate ODSV.

[0574] A large auxiliary fan for use in mining may be constructed for transportability into and through the mine and therefore may include a fan body, intake and outlet ports, dilution valves, protection cage, electrical enclosure, wheels, access panels, and other structural and/or operational elements. The ODSV of such an auxiliary fan may require collection of data from many different elements. A system for data collection may be configured to sense and collect data that may be combined with structural engineering data to facilitate ODSV for this type of industrial fan.

[0575] Referring to FIGS. 43 and 48, an embodiment of a system for data collection in an industrial environment that performs coordinated data collection suitable for ODSV is depicted. A system for data collection in an industrial environment may include a ODSV data collection template repository 7800 in which ODSV templates 7810 for data collection system configuration and collection of data may be stored and accessed by a system for data collection controller 7802. The templates 7810 may include data collection system configuration 7804 and operation information 7806 that may identify sensors, collectors, signal paths, reference signal information, information for initiation and coordination of collection, and the like. A controller 7802 may receive an indication, such as a command from a ODSV analysis facility 7808 to select and implement a specific ODSV template 7810. The controller 7802 may access the template 7810 and configure the data collection system resources based on the information in that template. In embodiments, the template may identify specific sensors, multiplexer/switch configuration, reference signals for coordinating data collection, data collection trigger/initiation signals and/or conditions, time duration, and/or amount of data for collection, destination of collected data, intermediate processing, if any, and any other useful information (e.g., instance identifier, and the like). The controller 7802 may configure and operate the data collection system to perform the collection for the ODSV template and optionally return the system configuration to a previous configuration.

[0576] An example method of data collection for performing ODSV in an industrial environment includes automatically configuring local and remote data collection resources and collecting data from a number of sensors using the configured resources, where the number of sensors include a group of sensors that produce data that is required to perform the ODSV. In certain further embodiments, an example method further includes where the sensors are distributed throughout structural portions of an industrial machine in the industrial environment; where the sensors sense a range of system conditions including vibration, rotation, balance, and/or friction; where the automatically configuring is in response to a condition in the environment being detected outside of an acceptable range of condition values; where the condition is sensed by a sensor in a group of system sensors; where automatically configuring includes configuring a signal switching resource to concurrently connect a portion of the group of sensors to data collection resources; and/or where the signal switching resource is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform ODSV.

[0577] An example method of data collection in an industrial environment includes configuring a data collection plan to collect data from a number of system sensors distributed throughout a machine in the industrial environment, the plan based on machine structural information and an indication of data needed to produce an ODSV of the machine; configuring data sensing, routing and collection resources in the environment based on the data collection plan; and collecting data based on the data collection plan. In certain further embodiments, an example method further includes: producing the ODSV; where the configuring data sensing, routing, and collection resources is in response to a condition in the environment being detected outside of an acceptable range of condition values; where the condition is sensed by a sensor identified in the data collection plan; where configuring resources includes configuring a signal switching resource to concurrently connect the plurality of system sensors to data collection resources; and/or where the signal switching resource is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform ODSV.

[0578] An example system for data collection in an industrial environment includes: a number of sensors disposed throughout the environment; multiplexer that connects signals from the plurality of sensors to data collection resources; and a processor for processing data collected from the number of sensors in response to the data collection template, where the processing results in an ODSV of a portion of a machine disposed in the environment. In certain further embodiments, an example system includes: where the ODSV collection template further identifies a condition in the environment on which performing data collection from the identified sensors is dependent; where the condition is sensed by a sensor identified in the ODSV data collection template; where the data collection template specified inputs of the multiplexer to concurrently connect to data collection resources; where the multiplexer is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform ODSV; where the ODSV data collection template specifies data collection requirements for performing ODSV for looseness, soft joints, bending, and/or twisting of a portion of a machine in the industrial environment; and/or where the ODSV collection template specifies an order and timing of data collection from a plurality of identified sensors.

[0579] An example method of monitoring a mining conveyer for performing ODSV of the conveyer includes automatically configuring local and remote data collection resources and collecting data from a number of sensors disposed to sense the mining conveyor using the configured resources, wherein the plurality of sensors comprise a group of sensors that produce data that is required to perform the operational deflection shape visualization of a portion of the conveyor. An example method of monitoring a mining fan for performing ODSV of the fan includes automatically configuring local and remote data collection resources collecting data from a number of sensors disposed to sense the fan using the configured resources, and where the number of sensors include a group of sensors that produce data that is sufficient or required to perform ODSV of a portion of the fan.

[0580] In embodiments, a system for data collection in an industrial environment may include a hierarchical multiplexer that facilitates successive multiplexing of input data channels according to a configurable hierarchy, such as a user configurable hierarchy. The system for data collection in an industrial environment may include the hierarchical multiplexer that facilitates successive multiplexing of a plurality of input data channels according to a configurable hierarchy. The hierarchy may be automatically configured by a controller based on an operational parameter in the industrial environment, such as a parameter of a machine in the industrial environment.

[0581] In embodiments, a system for data collection in an industrial environment may include a plurality of sensors that may output data at different rates. The system may also include a multiplexer module that receives sensor outputs from a first portion of the plurality of sensors with similar output rates into separate inputs of a first hierarchical multiplexer of the multiplexer module. The first hierarchical multiplexer of the multiplexer module may provide at least one multiplexed output of a portion of its inputs to a second hierarchical multiplexer that receives sensor outputs from a second portion of the plurality of sensors with similar output rates and that provides at least one multiplexed output of a portion of its inputs. In embodiments, the output rates of the first set of sensors may be slower than the output rates of the second set of sensors. In embodiments, data collection rate requirements of the first set of sensors may be lower than the data collection rate requirements of the second set of sensors. In embodiments, the first hierarchical multiplexer output is a time-multiplexed combination of a portion of its inputs. In embodiments, the second hierarchical multiplexer receives sensor signals with output rates that are similar to a rate of output of the first multiplexer, wherein the first multiplexer produces time-based multiplexing of the portion of its plurality of inputs.

[0582] In embodiments, a system for data collection in an industrial environment may include a hierarchical multiplexer that is dynamically configured based on a data acquisition template. The hierarchical multiplexer may include a plurality of inputs and a plurality of outputs, wherein any input can be directed to any output in response to sensor output collection requirements of the template, and wherein a subset of the inputs can be multiplexed at a first switching rate and output to at least one of the plurality of outputs.

[0583] In embodiments, a system for data collection in an industrial environment may include a plurality of sensors for sensing conditions of a machine in the environment, a hierarchical multiplexer, a plurality of analog-to-digital converters (ADCs), a processor, local storage, and an external interface. The system may use the processor to access a data acquisition template of parameters for data collection from a portion of the plurality of sensors, configure the hierarchical multiplexer, the ADCs and the local storage to facilitate data collection based on the defined parameters, and execute the data collection with the configured elements including storing a set of data collected from a portion of the plurality of sensors into the local storage. In embodiments, the ADCs convert analog sensor data into a digital form that is compatible with the hierarchical multiplexer. In embodiments, the processor monitors at least one signal generated by the sensors for a trigger condition and, upon detection of the trigger condition, responds by at least one of communicating an alert over the external interface and performing data acquisition according to a template that corresponds to the trigger condition.

[0584] In embodiments, a system for data collection in an industrial environment may include a hierarchical multiplexer that may be configurable based on a data collection template of the environment. The multiplexer may support receiving a large number of data signals (e.g., from sensors in the environment) simultaneously. In embodiments, all sensors for a portion of an industrial machine in the environment may be individually connected to inputs of a first stage of the multiplexer. The first stage of the multiplexer may provide a plurality of outputs that may feed into a second multiplexer stage. The second stage multiplexer may provide multiple outputs that feed into a third stage, and so on. Data collection templates for the environment may be configured for certain data collection sets, such as a set to determine temperature throughout a machine or a set to determine vibration throughout a machine, and the like. Each template may identify a plurality of sensors in the environment from which data is to be collected, such as during a data collection event. When a template is presented to the hierarchical multiplexer, mapping of inputs to outputs for each multiplexing stage may be configured so that the required data is available at output(s) of a final multiplexing hierarchical stage for data collection. In an example, a data collection template to collect a set of data to determine temperature throughout a machine in the environment may identify many temperature sensors. The first stage multiplexer may respond to the template by selecting all of the available inputs that connect to temperature sensors. The data from these sensors may be multiplexed onto multiple inputs of a second stage sensor that may perform time-based multiplexing to produce a time-multiplexed output(s) of temperature data from a portion of the sensors. These outputs may be gathered by a data collector and de-multiplexed into individual sensor temperature readings.

[0585] In embodiments, time-sensitive signals, such as triggers and the like, may connect to inputs that directly connect to a final multiplexer stage, thereby reducing any potential delay caused by routing through multiple multiplexing stages.

[0586] In embodiments, a hierarchical multiplexer in a system for data collection in an industrial environment may comprise an array of relays, a programmable logic component, such as a CPLD, a field programmable gate array (FPGA), and the like.

[0587] In embodiments, a system for data collection in an industrial environment that may include a hierarchical multiplexer for routing sensor outputs onto signal paths may be used with explosive systems in mining applications. Blast initiating and electronic blasting systems may be configured to provide computer assisted blasting systems. Ensuring that blasting occurs safely may involve effective sensing and analysis of a range of conditions. A system for data collection in an industrial environment may be deployed to sense and collect data associated with explosive systems, such as explosive systems used for mining. A data collection system can use a hierarchical multiplexer to capture data from explosive system installations automatically by aligning, for example, a deployment of the explosive system including its layout plans, integration, interconnectivity, cascading plan, and the like with the hierarchical multiplexer. An explosive system may be deployed with a form of hierarchy that starts with a primary initiator and follows detonation connections through successive layers of electronic blast control to sequenced detonation. Data collected from each of these layers of blast systems configuration may be associated with stages of a hierarchical multiplexer so that data collected from bulk explosive detonation can be captured in a hierarchy that corresponds to its blast control hierarchy.

[0588] In embodiments, a system for data collection in an industrial environment that may include a hierarchical multiplexer for routing sensor outputs onto signal paths may be used with refinery blowers in oil and gas pipeline applications. Refinery blower applications include fired heater combustion air preheat systems and the like. Forced draft blowers may include a range of moving and moveable parts that may benefit from condition sensing and monitoring. Sensing may include detecting conditions of: couplings (e.g., temperature, rotational rate, and the like); motors (vibration, temperature, RPMs, torque, power usage, and the like); louver mechanics (actuators, louvers, and the like); and plenums (flow rate, blockage, back pressure, and the like). A system for data collection in an industrial environment that uses a hierarchical multiplexer for routing signals from sensors and the like to data collectors may be configured to collect data from a refinery blower. In an example, a plurality of sensors may be deployed to sense air flow into, throughout, and out of a forced draft blower used in a refinery application, such as to preheat combustion air. Sensors may be grouped based on a frequency of a signal produced by sensors. Sensors that detect louver position and control may produce data at a lower rate than sensors that detect blower RPMs. Therefore, louver position and control sensor signals can be applied to a lower stage in a multiplexer hierarchy than the blower RPM sensors because data from louvers change less often than data from RPM sensors. A data collection system could switch among a plurality of louver sensors and still capture enough information to properly detect louver position. However, properly detecting blower RPM data may require greater bandwidth of connection between the blower RPM sensor and a data collector. A hierarchical multiplexer may enable capturing blower RPM data at a rate that is required for proper detection (perhaps by outputting the RPM sensor data for long durations of time), while switching among several louver sensor inputs and directing them onto (or through) an output that is different than the blower RPM output. Alternatively, the louver inputs may be time-multiplexed with the blower RPM data onto a single output that can be de-multiplexed by a data collector that is configured to determine when blower RPM data is being output and when louver position data is being output.

[0589] In embodiments, a system for data collection in an industrial environment that may include a hierarchical multiplexer for routing sensor outputs onto signal paths may be used with pipeline-related compressors (e.g., reciprocating) in oil and gas pipeline applications. A typical use of a reciprocating compressor for pipeline application is production of compressed air for pipeline testing. A system for data collection in an industrial environment may apply a hierarchical multiplexer while collecting data from a pipeline testing-based reciprocating compressor. Data from sensors deployed along a portion of a pipeline being tested may be input to the lowest stage of the hierarchical multiplexer because these sensors may be periodically sampled prior to and during testing. However, the rate of sampling may be low relative to sensors that detect compressor operation, such as parts of the compressor that operate at higher frequencies, such as the reciprocating linkage, motor, and the like. The sensors that provide data at frequencies that enable reproduction of the detected motion may be input to higher stages in the hierarchical multiplexer. Time multiplexing among the pipeline sensors may provide for coverage of a large number of sensors while capturing events such as seal leakage and the like. However, time multiplexing among reciprocating linkage sensors may require output signal bandwidth that may exceed the bandwidth available for routing data from the multiplexer to a data collector. Therefore, in embodiments, a plurality of pipeline sensors may be time-multiplexed onto a single multiplexer output and a compressor sensor detecting rapidly moving parts, such as the compressor motor, may be routed to separate outputs of the multiplexer.

[0590] Referring to FIG. 49, a system for data collection in an industrial environment that uses a hierarchical multiplexer for routing sensor signals to data collectors is depicted. Outputs from a plurality of sensors, such as sensors that monitor conditions that change with relatively low frequency (e.g., blower louver position sensors) may be input to a lowest hierarchical stage 8000 of a hierarchical multiplexer 8002 and routed to successively higher stages in the multiplexer, ultimately being output from the multiplexer, perhaps as a time-multiplexed signal comprising time-specific samples of each of the plurality of low frequency sensors. Outputs from a second plurality of sensors, such as sensors that monitor motor operation that may run at more than 1000 RPMs may be input to a higher hierarchical stage 8004 of the hierarchical multiplexer and routed to outputs that support the required bandwidth.

[0591] An example system for data collection in an industrial environment includes a controller for controlling data collection resources in the industrial environment and a hierarchical multiplexer that facilitates successive multiplexing of a number of input data channels according to a configurable hierarchy, wherein the hierarchy is automatically configured by the controller based on an operational parameter of a machine in the industrial environment. In certain further embodiments, an example system includes: where the operational parameter of the machine is identified in a data collection template; where the hierarchy is automatically configured in response to smart band data collection activation further including an analog-to-digital converter disposed between a source of the input data channels and the hierarchical multiplexer; and/or where the operational parameter of the machine comprises a trigger condition of at least one of the data channels. Another example system for data collection in an industrial environment includes a plurality of sensors and a multiplexer module that receives sensor outputs from a first portion of the sensors with similar output rates into separate inputs of a first hierarchical multiplexer that provides at least one multiplexed output of a portion of its inputs to a second hierarchical multiplexer, the second hierarchical multiplexer receiving sensor outputs from a second portion of the sensors and providing at least one multiplexed output of a portion of its inputs. In certain further embodiments, an example system includes: where the second portion of the sensors output data at rates that are higher than the output rates of the first portion of the sensors; where the first portion and the second portion of the sensors output data at different rates; where the first hierarchical multiplexer output is a time-multiplexed combination of a portion of its inputs; where the second multiplexer receives sensor signals with output rates that are similar to a rate of output of the first multiplexer; and/or where the first multiplexer produces time-based multiplexing of the portion of its inputs.

[0592] An example system for data collection in an industrial environment includes a number of sensors for sensing conditions of a machine in the environment a hierarchical multiplexer, a number of analog-to-digital converters, a controller, local storage, an external interface, where the system includes using the controller to access a data acquisition template that defines parameters for data collection from a portion of the sensors, to configure the hierarchical multiplexer, the ADCs, and the local storage to facilitate data collection based on the defined parameters, and to execute the data collection with the configured elements including storing a set of data collected from a portion of the sensors into the local storage. In certain further embodiments, an example system includes: where the ADCs convert analog sensor data into a digital form that is compatible with the hierarchical multiplexer; where the processor monitors at least one signal generated by the sensors for a trigger condition and, upon detection of the trigger condition, responds by communicating an alert over the external interface and/or performing data acquisition according to a template that corresponds to the trigger condition; where the hierarchical multiplexer performs successive multiplexing of data received from the sensors according to a configurable hierarchy; where the hierarchy is automatically configured by the controller based on an operational parameter of a machine in the industrial environment; where the operational parameter of the machine is identified in a data collection template; where the hierarchy is automatically configured in response to smart band data collection activation; the system further including an ADC disposed between a source of the input data channels and the hierarchical multiplexer; where the operational parameter of the machine includes a trigger condition of at least one of the data channels; where the hierarchical multiplexer performs successive multiplexing of data received from the plurality of sensors according to a configurable hierarchy; and/or where the hierarchy is automatically configured by a controller based on a detected parameter of an industrial environment. Without limitation, n example system is configured for monitoring a mining explosive system, and includes a controller for controlling data collection resources associated with the explosive system, and a hierarchical multiplexer that facilitates successive multiplexing of a number of input data channels according to a configurable hierarchy, where the hierarchy is automatically configured by the controller based on a configuration of the explosive system. Without limitation, an example system is configured for monitoring a refinery blower in an oil and gas pipeline applications, and includes a controller for controlling data collection resources associated with the refinery blower, and a hierarchical multiplexer that facilitates successive multiplexing of a number of input data channels according to a configurable hierarchy, where the hierarchy is automatically configured by the controller based on a configuration of the refinery blower. Without limitation, an example system is configured for monitoring a reciprocating compressor in an oil and gas pipeline applications comprising, and includes controller for controlling data collection resources associated with the reciprocating compressor, and a hierarchical multiplexer that facilitates successive multiplexing of a number of input data channels according to a configurable hierarchy, where the hierarchy is automatically configured by the controller based on a configuration of the reciprocating compressor.

[0593] In embodiments, a system for data collection in an industrial environment may include an ultrasonic sensor disposed to capture ultrasonic conditions of an element of in the environment. The system may be configured to collect data representing the captured ultrasonic condition in a computer memory, on which a processor may execute an ultrasonic analysis algorithm. In embodiments, the sensed element may be one of a moving element, a rotating element, a structural element, and the like. In embodiments, the data may be streamed to the computer memory. In embodiments, the data may be continuously streamed. In embodiments, the data may be streamed for a duration of time, such as an ultrasonic condition sampling duration. In embodiments, the system may also include a data routing infrastructure that facilitates routing the streaming data from the ultrasonic sensor to a plurality of destinations including local and remote destinations. The routing infrastructure may include a hierarchical multiplexer that is adapted to route the streaming data and data from at least one other sensor to a destination.

[0594] In embodiments, ultrasonic monitoring in an industrial environment may be performed by a system for data collection as described herein on rotating elements (e.g., motor shafts and the like), bearings, fittings, couplings, housings, load bearing elements, and the like. The ultrasonic data may be used for pattern recognition, state determination, time-series analysis, and the like, any of which may be performed by computing resources of the industrial environment, which may include local computing resources (e.g., resources located within the environment and/or within a machine in the environment, and the like) and remote computing resources (e.g., cloud-based computing resources, and the like).

[0595] In embodiments, ultrasonic monitoring in an industrial environment by a system for data collection may be activated in response to a trigger (e.g., a signal from a motor indicating the motor is operational, and the like), a measure of time (e.g., an amount of time since the most recent monitoring activity, a time of day, a time relative to a trigger, an amount of time until a future event, such as machine shutdown, and the like), an external event (e.g., lightning strike, and the like). The ultrasonic monitoring may be activated in response to implementation of a smart band data collection activity. The ultrasonic monitoring may be activated in response to a data collection template being applied in the industrial environment. The data collection template may be configured based on analysis of prior vibration-caused failures that may be applicable to the monitored element, machine, environment, and the like. Because continuous monitoring of ultrasonic data may require dedicating data routing resources in the industrial environment for extended periods of time, a data collection template for continuous ultrasonic monitoring may be configured with data routing and resource utilization setup information that a controller of a data collection system may use to setup the resources to accommodate continuous ultrasonic monitoring. In an example, a data multiplexer may be configured to dedicate a portion of its outputs to the ultrasonic data for a duration of time specified in the template.

[0596] In embodiments, a system for data collection in an industrial environment may perform continuous ultrasonic monitoring. The system may also include processing of the ultrasonic data by a local processor located proximal to the vibration monitoring sensor or device(s). Depending on the computing capabilities of the local processor, functions such as peak detection may be performed. A programmable logic component may provide sufficient computing capabilities to perform peak detection. Processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control loop to potentially adjust an operating condition, such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis.

[0597] In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring, and in particular, continuous ultrasonic monitoring. The ultrasonic monitoring data may be combined with multi-dimensional models of an element or machine being monitored to produce a visualization of the ultrasonic data. In embodiments, an image, set of images, video, and the like may be produced that correlates in time with the sensed ultrasonic data. In embodiments, image recognition and/or analysis may be applied to ultrasonic visualizations to further facilitate determining the severity of a condition detected by the ultrasonic monitoring. The image analysis algorithms may be trained to detect normal and out of bounds conditions. Data from load sensors may be combined with ultrasonic data to facilitate testing materials and systems.

[0598] In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring of a pipeline in an oil and gas pipeline application. Flows of petroleum through pipelines can create vibration and other mechanical effects that may contribute to structural changes in a liner of the pipeline, support members, flow boosters, regulators, diverters, and the like. Performing continuous ultrasonic monitoring of key elements in a pipeline may facilitate detecting early changes in material, such as joint fracturing, and the like, that may lead to failure. A system for data collection in an industrial environment may be configured with ultrasonic sensing devices that may be connected through signal data routing resources, such as crosspoint switches, multiplexers, and the like, to data collection and analysis nodes at which the collected ultrasonic data can be collected and analyzed. In embodiments, a data collection system may include a controller that may reference a data collection plan or template that includes information to facilitate configuring the data sampling, routing, and collection resources of the system to accommodate collecting ultrasonic sample data from a plurality of elements along the pipeline. The template may indicate a sequence for collecting ultrasonic data from a plurality of ultrasonic sensors and the controller may configure a multiplexer to route ultrasonic sensor data from a specified ultrasonic sensor to a destination, such as a data storage controller, analysis processor and the like, for a duration specified in the template. The controller may detect a sequence of collection in the template, or a sequence of templates to access, and respond to each template in the detected sequence, adjusting the multiplexer and the like to route the sensor data specified in each template to a collector.

[0599] In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring of compressors in a power generation application. Compressors include several critical rotating elements (e.g., shaft, motor, and the like), rotational support elements (e.g., bearings, couplings, and the like), and the like. A system for data collection configured to facilitate sensing, routing, collection and analysis of ultrasonic data in a power generation application may receive ultrasonic sensor data from a plurality of ultrasonic sensors. Based on a configuration setup template, such as a template for collecting continuous ultrasonic data from one or more ultrasonic sensor devices, a controller may configure resources of the data collection system to facilitate delivery of the ultrasonic data over one or more signal data lines from the sensor(s) at least to data collectors that may be locally or remotely accessible. In embodiments, a template may indicate that ultrasonic data for a main shaft should be retrieved continuously for one minute, and then ultrasonic data for a secondary shaft should be retrieved for another minute, followed by ultrasonic data for a housing of the compressor. The controller may configure a multiplexer that receives the ultrasonic data for each of these sensors to route the data from each sensor in order by configuring a control set that initially directs the inputs from the main shaft ultrasonic sensors through the multiplexer until the time or other measure of data being forwarded is reached. The controller could switch the multiplexer to route the additional ultrasonic data as required to satisfy the second template requirements. The controller may continue adjusting the data collection system resources along the way until all of the ultrasonic monitoring data collection templates are satisfied.

[0600] In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring of wind turbine gearboxes in a wind energy generation application. Gearboxes in wind turbines may experience a high degree of resistance in operation, due in part to the changing nature of wind, which may cause moving parts, such as the gear planes, hydraulic fluid pumps, regulators, and the like, to prematurely fail. A system for data collection in an industrial environment may be configured with ultrasonic sensors that capture information that may lead to early detection of potential failure modes of these high-strain elements. To ensure that ultrasonic data may be effectively acquired from several different ultrasonic sensors with sufficient coverage to facilitate producing an actionable ultrasonic imaging assessment, the system may be configured specifically to deliver sufficient data at a relatively high rate from one or more of the sensors. Routing channel(s) may be dedicated to transferring ultrasonic sensing data for a duration of time that may be specified in an ultrasonic data collection plan or template. To accomplish this, a controller, such as a programmable logic component, may configure a portion of a crosspoint switch and data collectors to deliver ultrasonic data from a first set of ultrasonic sensors (e.g., those that sense hydraulic fluid flow control elements) to a plurality of data collectors. Another portion of the crosspoint switch may be configured to route additional sensor data that may be useful for evaluating the ultrasonic data (e.g., motor on/off state, thermal condition of sensed parts, and the like) on other data channels to data collectors where the data can be combined and analyzed. The controller may reconfigure the data routing resources to enable collecting ultrasonic data from other elements based on a corresponding data collection template.

[0601] Referring to FIG. 50, a system for data collection in an industrial environment may include one or more ultrasonic sensors 8050 that may connect to a data collection and routing system 8052 that may be configured by a controller 8054 based on an ultrasonic sensor-specific data collection template 8056 that may be provided to the controller 8054 by an ultrasonic data analysis facility 8058. The controller 8054 may configure resources of the data collection system 8052 and monitor the data collection for a duration of time based on the requirements for data collection in the template 8056.

[0602] An example system for data collection in an industrial environment includes an ultrasonic sensor disposed to capture ultrasonic conditions of an element in the environment, a controller that configures data routing resources of the data collection system to route ultrasonic data being captured by the ultrasonic sensor to a destination location that is specified by an ultrasonic monitoring data collection template, and a processor executing an ultrasonic analysis algorithm on the data after arrival at the destination. In certain further embodiments, an example system includes: where the template defines a time interval of continuous ultrasonic data capture from the ultrasonic sensor; a data routing infrastructure that facilitates routing the streaming data from the ultrasonic sensor to a number of destinations including local and remote destinations; the routing infrastructure including a hierarchical multiplexer that is adapted to route the streaming data and data from at least one other sensor to a destination; where the element in the environment includes rotating elements, bearings, fittings, couplings, housing, and/or load bearing parts; where the template defines a condition of activation of continuous ultrasonic monitoring; and/or where the condition of activation includes a trigger, a smart-band, a template, an external event, and/or a regulatory compliance configuration.

[0603] An example system for data collection in an industrial environment includes an ultrasonic sensor disposed to capture ultrasonic conditions of an element of an industrial machine in the environment, a controller that configures data routing resources of the data collection system to route ultrasonic data being captured by the ultrasonic sensor to a destination location that is specified by an ultrasonic monitoring data collection template, and a processor executing an ultrasonic analysis algorithm on the data after arrival at the destination. In certain embodiments, an example system further includes: wherein the template defines a time interval of continuous ultrasonic data capture from the ultrasonic sensor; the system further including a data routing infrastructure that facilitates routing the data from the ultrasonic sensor to a number of destinations including local and remote destinations; the data routing infrastructure including a hierarchical multiplexer that is adapted to route the ultrasonic data and data from at least one other sensor to a destination; where the element of the industrial machine includes rotating elements, bearings, fittings, couplings, housing, and/or load bearing parts; where the template defines a condition of activation of continuous ultrasonic monitoring; and/or where the condition of activation includes a trigger, a smart-band, a template, an external event, and/or a regulatory compliance configuration.

[0604] An example method of continuous ultrasonic monitoring in an industrial environment includes disposing an ultrasonic monitoring device within ultrasonic monitoring range of at least one moving part of an industrial machine in the industrial environment, the ultrasonic monitoring device producing a stream of ultrasonic monitoring data, configuring, based on an ultrasonic monitoring data collection template, a data routing infrastructure to route the stream of ultrasonic monitoring data to a destination, where the infrastructure facilitates routing data from a number of sensors through at an analog crosspoint switch and/or a hierarchical multiplexer, to a number of destinations, routing the ultrasonic monitoring device data through the routing infrastructure to a destination; processing the stored data with an ultrasonic data analysis algorithm that provides an ultrasonic analysis of at least one of a motor shaft, bearings, fittings, couplings, housing, and load bearing parts; and/or storing the data in a computer accessible memory at the destination. Certain further embodiments of an example method include: where the data collection template defines a time interval of continuous ultrasonic data capture from the ultrasonic monitoring device; where configuring the data routing infrastructure includes configuring the hierarchical multiplexer to route the ultrasonic data and data from at least one other sensor to a destination; where ultrasonic monitoring is performed on at least one element in an industrial machine that includes rotating elements, bearings, fittings, couplings, a housing, and/or load bearing parts; where the template defines a condition of activation of continuous ultrasonic monitoring; where the condition of activation includes a trigger, a smart-band, a template, an external event, and/or a regulatory compliance configuration; where the ultrasonic data analysis algorithm performs pattern recognition; and/or where routing the ultrasonic monitoring device data is in response to detection of a condition in the industrial environment associated with the at least one moving part.

[0605] Without limitation, an example system for monitoring an oil or gas pipeline includes a processor executing an ultrasonic analysis algorithm on the pipeline data after arrival at the destination; an example system for monitoring a power generation compressor includes a processor executing an ultrasonic analysis algorithm on the power generation compressor data after arrival at the destination; and an example system for monitoring a wind turbine gearbox includes a processor executing an ultrasonic analysis algorithm on the gearbox data after arrival at the destination.

[0606] Industrial components such as pumps, compressors, air conditioning units, mixers, agitators, motors, and engines may play critical roles in the operation of equipment in a variety of environments including as part of manufacturing equipment in industrial environments such as factories, gas handling systems, mining operations, automotive systems, and the like.

[0607] There are a wide variety of pumps such as a variety of positive displacement pumps, velocity pumps, and impulse pumps. Velocity or centrifugal pumps typically comprise an impeller with curved blades which, when an impeller is immersed in a fluid, such as water or a gas, causes the fluid or gas to rotate in the same rotational direction as the impeller. As the fluid or gas rotates, centrifugal force causes it to move to the outer diameter of the pump, e.g., the pump housing, where it can be collected and further processed. The removal of the fluid or gas from the outer circumference may result in lower pressure at a pump input orifice causing new fluid or gas to be drawn into the pump.

[0608] Positive displacement pumps may comprise reciprocating pumps, progressive cavity pumps, gear or screw pumps, such as reciprocating pumps typically comprise a piston which alternately creates suction, which opens an inlet valve and draws a liquid or gas into a cylinder, and pressure, which closes the inlet valve and forces the liquid or gas present out of the cylinder through an outlet valve. This method of pumping may result in periodic waves of pressurized liquid or gas being introduced into the downstream system.

[0609] Some automotive vehicles such as cars and trucks may use a water cooling system to keep the engine from overheating. In some automobiles, a centrifugal water pump, driven by a belt associated with a driveshaft of the vehicle, is used to force a mixture of water and coolant through the engine to maintain an acceptable engine temperature. Overheating of the engine may be highly destructive to the engine and yet it may be difficult or costly to access a water pump installed in a vehicle.

[0610] In embodiments, a vehicle water pump may be equipped with a plurality of sensors for measuring attributes associated with the water pump such as temperature of bearings or pump housing, vibration of a driveshaft associated with the pump, liquid leakage, and the like. These sensors may be connected either directly to a monitoring device or through an intermediary device using a mix of wired and wireless connection techniques. A monitoring device may have access to detection values corresponding to the sensors where the detection values correspond directly to the sensor output or a processed version of the data output such as a digitized or sampled version of the sensor output, and/or a virtual sensor or modeled value correlated from other sensed values. The monitoring device may access and process the detection values using methods discussed elsewhere herein to evaluate the health of the water pump and various components of the water pump prone to wear and failure, e.g., bearings or sets of bearings, drive shafts, motors, and the like. The monitoring device may process the detection values to identify a torsion of the drive shaft of the pump. The identified torsion may then be evaluated relative to expected torsion based on the specific geometry of the water pump and how it is installed in the vehicle. Unexpected torsion may put undue stress on the driveshaft and may be a sign of deteriorating health of the pump. The monitoring device may process the detection values to identify unexpected vibrations in the shaft or unexpected temperature values or temperature changes in the bearings or in the housing in proximity to the bearings. In some embodiments, the sensors may include multiple temperature sensors positioned around the water pump to identify hot spots among the bearings or across the pump housing which might indicate potential bearing failure. The monitoring device may process the detection values associated with water sensors to identify liquid leakage near the pump which may indicate a bad seal. The detection values may be jointly analyzed to provide insight into the health of the pump.

[0611] In an illustrative example, detection values associated with a vehicle water pump may show a sudden increase in vibration at a higher frequency than the operational rotation of the pump with a corresponding localized increase of temperature associated with a specific phase in the pump cycle. Together these may indicate a localized bearing failure.

[0612] Production lines may also include one or more pumps for moving a variety of material including acidic or corrosive materials, flammable materials, minerals, fluids comprising particulates of varying sizes, high viscosity fluids, variable viscosity fluids, or high-density fluids. Production line pumps may be designed to specifically meet the needs of the production line including pump composition to handle the various material types, or torque needed to move the fluid at the desired speed or with the desired pressure. Because these production lines may be continuous process lines, it may be desirable to perform proactive maintenance rather than wait for a component to fail. Variations in pump speed and pressure may have the potential to negatively impact the final product, and the ability to identify issues in the final product may lag the actual component deterioration by an unacceptably long period.

[0613] In embodiments, an industrial pump may be equipped with a plurality of sensors for measuring attributes associated with the pump such as temperature of bearings or pump housing, vibration of a driveshaft associated with the pump, vibration of input or output lines, pressure, flow rate, fluid particulate measures, vibrations of the pump housing, and the like. These sensors may be connected either directly to a monitoring device or through an intermediary device using a mix of wired and wireless connection techniques. A monitoring device may have access to detection values corresponding to the sensors where the detection values correspond directly to the sensor output of a processed version of the data output such as a digitized or sampled version of the sensor output. The monitoring device may access and process the detection values using methods discussed elsewhere herein to evaluate the health of the pump overall, evaluate the health of pump components, predict potential down line issues arising from a typical pump performance, or changes in fluid being pumped. The monitoring device may process the detection values to identify torsion on the drive shaft of the pump. The identified torsion may then be evaluated relative to expected torsion based on the specific geometry of the pump and how it is installed in the equipment relative to other components on the assembly line. Unexpected torsion may put undue stress on the driveshaft and may be a sign of deteriorating health of the pump. Vibration of the inlet and outlet pipes may also be evaluated for unexpected or resonant vibrations which may be used to drive process controls to avoid certain pump frequencies. Changes in vibration may also be due to changes in fluid composition or density, amplifying or dampening vibrations at certain frequencies. The monitoring device may process the detection values to identify unexpected vibrations in the shaft, unexpected temperature values, or temperature changes in the bearings or in the housing in proximity to the bearings. In some embodiments, the sensors may include multiple temperature sensors positioned around the pump to identify hot spots among the bearings or across the pump housing which might indicated potential bearing failure. For some pumps, when the fluid being pumped is corrosive or contains large amounts of particulates, there may be damage to the interior components of the pump in contact with the fluid due to cumulative exposure to the fluid. This may be reflected in unanticipated variations in output pressure. Additionally or alternatively, if a gear in a gear pump begins to corrode and no longer forces all the trapped fluid out this may result in increased pump speed, fluid cavitation, and/or unexpected vibrations in the output pipe.

[0614] Compressors increase the pressure of a gas by decreasing the volume occupied by the gas or increasing the amount of the gas in a confined volume. There may be positive-displacement compressors that utilize the motion of pistons or rotary screws to move the gas into a pressurized holding chamber. There are dynamic displacement gas compressors that use centrifugal force to accelerate the gas into a stationary compressor where the kinetic energy is converted to pressure. Compressors may be used to compress various gases for use on an assembly line. Compressed air may power pneumatic equipment on an assembly line. In the oil and gas industry, flash gas compressors may be used to compress gas so that it leaves a hydrocarbon liquid when it enters a lower pressure environment. Compressors may be used to restore pressure in gas and oil pipelines, to mix fluids of interest, and/or to transfer or transport fluids of interest. Compressors may be used to enable the underground storage of natural gas.

[0615] Like pumps, compressors may be equipped with a plurality of sensors for measuring attributes associated with the compressor such as temperature of bearings or compressor housing, vibration of a driveshaft, transmission, gearbox and the like associated with the compressor, vessel pressure, flow rate, and the like. These sensors may be connected either directly to a monitoring device or through an intermediary device using a mix of wired and wireless connection techniques. A monitoring device may have access to detection values corresponding to the sensors where the detection values correspond directly to the sensor output of a processed version of the data output such as a digitized or sampled version of the sensor output. The monitoring device may access and process the detection values using methods described elsewhere herein to evaluate the health of the compressor overall, evaluate the health of compressor components and/or predict potential down line issues arising from a typical compressor performance. The monitoring device may process the detection values to identify torsion on a driveshaft of the compressor. The identified torsion may then be evaluated relative to expected torsion based on the specific geometry of the compressor and how it is installed in the equipment relative to other components and pieces of equipment. Unexpected torsion may put undue stress on the driveshaft and may be a sign of deteriorating health of the compressor. Vibration of the inlet and outlet pipes may also be evaluated for unexpected or resonant vibrations which may be used to drive process controls to avoid certain compressor frequencies. The monitoring device may process the detection values to identify unexpected vibrations in the shaft, unexpected temperature values or temperature changes in the bearings or in the housing in proximity to the bearings. In some embodiments, the sensors may include multiple temperature sensors positioned around the compressor to identify hot spots among the bearings or across the compressor housing, which might indicate potential bearing failure. In some embodiments, sensors may monitor the pressure in a vessel storing the compressed gas. Changes in the pressure or rate of pressure change may be indicative of problems with the compressor.

[0616] Agitators and mixers are used in a variety of industrial environments. Agitators may be used to mix together different components such as liquids, solids, or gases. Agitators may be used to promote a more homogenous mixture of component materials. Agitators may be used to promote a chemical reaction by increasing exposure between different component materials and adding energy to the system. Agitators may be used to promote heat transfer to facilitate uniform heating or cooling of a material.

[0617] Mixers and agitators are used in such diverse industries as chemical production, food production, pharmaceutical production, and the like. There are paint and coating mixers, adhesive and sealant mixers, oil and gas mixers, water treatment mixers, wastewater treatment mixers, and the like.

[0618] Agitators may comprise equipment that rotates or agitates an entire tank or vessel in which the materials to be mixed are located, such as a concrete mixer. Effective agitations may be influenced by the number and shape of baffles in the interior of the tank. Agitation by rotation of the tank or vessel may be influenced by the axis of rotation relative to the shape of the tank, direction of rotation, and external forces such as gravity acting on the material in the tank. Factors affecting the efficacy of material agitation or mixing by agitation of the tank or vessel may include axes of rotation, and amplitude and frequency of vibration along different axes. These factors may be selected based on the types of materials being selected, their relative viscosities, specific gravities, particulate count, any shear thinning or shear thickening anticipated for the component materials or mixture, flow rates of material entering or exiting the vessel or tank, direction and location of flows of material entering of exiting the vessel, and the like.

[0619] Agitators, large tank mixers, portable tank mixers, tote tank mixers, drum mixers, and mounted mixers (with various mount types) may comprise a propeller or other mechanical device such as a blade, vane, or stator inserted into a tank of materials to be mixed, while rotating a propeller or otherwise moving a mechanical device. These may include airfoil impellers, fixed pitch blade impellers, variable pitch blade impellers, anti-ragging impellers, fixed radial blade impellers, marine-type propellers, collapsible airfoil impellers, collapsible pitched blade impellers, collapsible radial blade impellers, and variable pitch impellers. Agitators may be mounted such that the mechanical agitation is centered in the tank. Agitators may be mounted such that they are angled in a tank or are vertically or horizontally offset from the center of the vessel. The agitators may enter the tank from above, below, or the side of the tank. There may be a plurality of agitators in a single tank to achieve uniform mixing throughout the tank or container of chemicals.

[0620] Agitators may include the strategic flow or introduction of component materials into the vessel including the location and direction of entry, rate of entry, pressure of entry, viscosity of material, specific gravity of the material, and the like.

[0621] Successful agitation of mixing of materials may occur with a combination of techniques such as one or more propellers in a baffled tank where components are being introduced at different locations and at different rates.

[0622] In embodiments, an industrial mixer or agitator may be equipped with a plurality of sensors for measuring attributes associated with the industrial mixer such as: temperature of bearings or tank housing, vibration of driveshafts associated with a propeller or other mechanical device such as a blade, vane or stator, vibration of input or output lines, pressure, flow rate, fluid particulate measures, vibrations of the tank housing and the like. These sensors may be connected either directly to a monitoring device or through an intermediary device using a mix of wired and wireless connection techniques. A monitoring device may have access to detection values corresponding to the sensors where the detection values correspond directly to the sensor output of a processed version of the data, output such as a digitized or sampled version of the sensor output, fusion of data from multiple sensors, and the like. The monitoring device may access and process the detection values using methods discussed elsewhere herein to evaluate the health of the agitator or mixer overall, evaluate the health of agitator or mixer components, predict potential down line issues arising from a typical performance or changes in composition of material being agitated. For example, the monitoring device may process the detection values to identify torsion on the driveshaft of an agitating impeller. The identified torsion may then be evaluated relative to expected torsion based on the specific geometry of the agitator and how it is installed in the equipment relative to other components and/or pieces of equipment. Unexpected torsion may put undue stress on the driveshaft and may be a sign of deteriorating health of the agitator. Vibration of inflow and outflow pipes may be monitored for unexpected or resonant vibrations which may be used to drive process controls to avoid certain agitation frequencies. Inflow and outflow pipes may also be monitored for unexpected flow rates, unexpected particulate content, and the like. Changes in vibration may also be due to changes in fluid composition, or density amplifying or dampening vibrations at certain frequencies. The monitoring device may distribute sensors to collect detection values which may be used to identify unexpected vibrations in the shaft, or unexpected temperature values or temperature changes in the bearings or in the housing in proximity to the bearings. For some agitators, when the fluid being agitated is corrosive or contains large amounts of particulates, there may be damage to the interior components of the agitator (e.g., baffles, propellers, blades, and the like) which are in contact with the materials, due to cumulative exposure to the materials.

[0623] HVAC, air-conditioning systems, and the like may use a combination of compressors and fans to cool and circulate air in industrial environments. Similar to the discussion of compressors and agitators, these systems may include a number of rotating components whose failure or reduced performance might negatively impact the working environment and potentially degrade product quality. A monitoring device may be used to monitor sensors measuring various aspects of the one or more rotating components, the venting system, environmental conditions, and the like. Components of the HVAC/air-conditioning systems may include fan motors, driveshafts, bearings, compressors, and the like. The monitoring device may access and process the detection values corresponding to the sensor outputs according to methods discussed elsewhere herein to evaluate the overall health of the air-conditioning unit, HVAC system, and like as well as components of these systems, identify operational states, predict potential issues arising from a typical performance, and the like. Evaluation techniques may include bearing analysis, torsional analysis of driveshafts, rotors and stators, peak value detection, and the like. The monitoring device may process the detection values to identify issues such as torsion on a driveshaft, potential bearing failures, and the like.

[0624] Assembly line conveyors may comprise a number of moving and rotating components as part of a system for moving material through a manufacturing process. These assembly line conveyors may operate over a wide range of speeds. These conveyances may also vibrate at a variety of frequencies as they convey material horizontally to facilitate screening, grading, laning for packaging, spreading, dewatering, feeding product into the next in-line process, and the like.

[0625] Conveyance systems may include engines or motors, one or more driveshafts turning rollers or bearings along which a conveyor belt may move. A vibrating conveyor may include springs and a plurality of vibrators which vibrate the conveyor forward in a sinusoidal manner.

[0626] In embodiments, conveyors and vibrating conveyors may be equipped with a plurality of sensors for measuring attributes associated with the conveyor such as temperature of bearings, vibration of driveshafts, vibrations of rollers along which the conveyor travels, velocity and speed associated with the conveyor, and the like. The monitoring device may access and process the detection values using methods discussed elsewhere herein to evaluate the overall health of the conveyor as well as components of the conveyor, predict potential issues arising from a typical performance, and the like. Techniques for evaluating the conveyors may include bearing analysis, torsional analysis, phase detection/phase lock loops to align detection values from different parts of the conveyor, frequency transformations and frequency analysis, peak value detection, and the like. The monitoring device may process the detection values to identify torsion on a driveshaft, potential bearing failures, uneven conveyance and like.

[0627] In an illustrative example, a paper-mill conveyance system may comprise a mesh onto which the paper slurry is coated. The mesh transports the slurry as liquid evaporates and the paper dries. The paper may then be wound onto a core until the roll reaches diameters of up to three meters. The transport speeds of the paper-mill range from traditional equipment operating at 14-48 meters/minute to new, high-speed equipment operating at close to 2000 meters/minute. For slower machines, the paper may be winding onto the roll at 14 meters/minute which, towards the end of the roll having a diameter of approximately three meters would indicate that the take up roll may be rotating at speeds on the order of a couple of rotations a minute. Vibrations in the web conveyance or torsion across the take up roller may result in damage to the paper, skewing of the paper on the web, or skewed rolls which may result in equipment downtime or product that is lower in quality or unusable. Additionally, equipment failure may result in costly machine shutdowns and loss of product. Therefore, the ability to predict problems and provide preventative maintenance and the like may be useful.

[0628] Monitoring truck engines and steering systems to facilitate timely maintenance and avoid unexpected breakdowns may be important. Health of the combustion chamber, rotating crankshafts, bearings, and the like may be monitored using a monitoring device structured to interpret detection values received from a plurality of sensors measuring a variety of characteristics associated with engine components including temperature, torsion, vibration, and the like. As discussed above, the monitoring device may process the detection values to identify engine bearing health, torsional vibrations on a crankshaft/driveshaft, unexpected vibrations in the combustion chambers, overheating of different components, and the like. Processing may be done locally or data may be collected across a number of vehicles and jointly analyzed. The monitoring device may process detection values associated with the engine, combustion chambers, and the like. Sensors may monitor temperature, vibration, torsion, acoustics, and the like to identify issues. A monitoring device or system may use techniques such as peak detection, bearing analysis, torsion analysis, phase detection, PLL, band pass filtering, and the like to identify potential issues with the steering system and bearing and torsion analysis to identify potential issues with rotating components on the engine. This identification of potential issues may be used to schedule timely maintenance, reduce operation prior to maintenance, and influence future component design.

[0629] Drilling machines and screwdrivers in the oil and gas industries may be subjected to significant stresses. Because they are frequently situated in remote locations, an unexpected breakdown may result in extended down time due to the lead-time associated with bringing in replacement components. The health of a drilling machine or screwdriver and associated rotating crankshafts, bearings, and the like may be monitored using a monitoring device structured to interpret detection values received from a plurality of sensors measuring a variety of characteristics associated with the drilling machine or screwdriver including temperature, torsion, vibration, rotational speed, vertical speed, acceleration, image sensors, and the like. As discussed above, the monitoring device may process the detection values to identify equipment health, torsional vibrations on a crankshaft/driveshaft, unexpected vibrations in the component, overheating of different components, and the like. Processing may be done locally or data collected across a number of machines and jointly analyzed. The monitoring device may jointly process detection values, equipment maintenance records, product records, historical data, and the like to identify correlations between detection values, current and future states of the component, anticipated lifetime of the component or piece of equipment, and the like. Sensors may monitor temperature, vibration, torsion, acoustics, and the like to identify issues such as unanticipated torsion in the drill shaft, slippage in the gears, overheating, and the like. A monitoring device or system may use techniques such as peak detection, bearing analysis, torsion analysis, phase detection, PLL, band pass filtering, and the like to identify potential issues. This identification of potential issues may be used to schedule timely maintenance, order new or replacement components, reduce operation prior to maintenance, and influence future component design.

[0630] Similarly, it may be desirable to monitor the health of gearboxes operating in an oil and gas field. A monitoring device may be structured to interpret detection values received from a plurality of sensors measuring a variety of characteristics associated with the gearbox such as temperature, vibration, and the like. The monitoring device may process the detection values to identify gear and gearbox health and anticipated life. Processing may be done locally or data collected across a number of gearboxes and jointly analyzed. The monitoring device may jointly process detection values, equipment maintenance records, product records historical data, and the like to identify correlations between detection values, current and future states of the gearbox, anticipated lifetime of the gearbox and associated components, and the like. A monitoring device or system may use techniques such as peak detection, bearing analysis, torsion analysis, phase detection, PLL, band pass filtering, to identify potential issues. This identification of potential issues may be used to schedule timely maintenance, order new or replacement components, reduce operation prior to maintenance, and influence future equipment design.

[0631] Refining tanks in the oil and gas industries may be subjected to significant stresses due to the chemical reactions occurring inside. Because a breach in a tank could result in the release of potentially toxic chemicals, it may be beneficial to monitor the condition of the refining tank and associated components. Monitoring a refining tank to collect a variety of ongoing data may be used to predict equipment wear, component wear, unexpected stress, and the like. Given predictions about equipment health, such as the status of a refining tank, may be used to schedule timely maintenance, order new or replacement components, reduce operation prior to maintenance, and influence future component design. Similar to the discussion above, a refining tank may be monitored using a monitoring device structured to interpret detection values received from a plurality of sensors measuring a variety of characteristics associated with the refining tank such as temperature, vibration, internal and external pressure, the presence of liquid or gas at seams and ports, and the like. The monitoring device may process the detection values to identify equipment health, unexpected vibrations in the tank, overheating of the tank or uneven heating across the tank, and the like. Processing may be done locally or data collected across a number of tanks and jointly analyzed. The monitoring device may jointly process detection values, equipment maintenance records, product records historical data, and the like to identify correlations between detection values, current and future states of the tank, anticipated lifetime of the tank and associated components, and the like. A monitoring device or system may use techniques such as peak detection, bearing analysis, torsion analysis, phase detection, PLL, band pass filtering, and the like to identify potential issues.

[0632] Similarly, it may be desirable to monitor the health of centrifuges operating in an oil and gas refinery. A monitoring device may be structured to interpret detection values received from a plurality of sensors measuring a variety of characteristics associated with the centrifuge such as temperature, vibration, pressure, and the like. The monitoring device may process the detection values to identify equipment health, unexpected vibrations in the centrifuge, overheating, pressure across the centrifuge, and the like. Processing may be done locally or data collected across a number of centrifuges and jointly analyzed. The monitoring device may jointly process detection values, equipment maintenance records, product records historical data, and the like to identify correlations between detection values, current and future states of the centrifuge, anticipated lifetime of the centrifuge and associated components, and the like. A monitoring device or system may use techniques such as peak detection, bearing analysis, torsion analysis, phase detection, PLL, band pass filtering, to identify potential issues. This identification of potential issues may be used to schedule timely maintenance, order new or replacement components, reduce operation prior to maintenance and influence future equipment design.

[0633] In embodiments, information about the health or other status or state information of or regarding a component or piece of industrial equipment may be obtained by monitoring the condition of various components throughout a process. Monitoring may include monitoring the amplitude of a sensor signal measuring attributes such as temperature, humidity, acceleration, displacement, and the like. An embodiment of a data monitoring device 8100 is shown in FIG. 51 and may include a plurality of sensors 8106 communicatively coupled to a controller 8102. The controller 8102 may include a data acquisition circuit 8104, a data analysis circuit 8108, a MUX control circuit 8114, and a response circuit 8110. The data acquisition circuit 8104 may include a MUX 8112 where the inputs correspond to a subset of the detection values. The MUX control circuit 8114 may be structured to provide adaptive scheduling of the logical control of the MUX and the correspondence of MUX input and detected values based on a subset of the plurality of detection values and/or a command from the response circuit 8110 and/or the output of the data analysis circuit 8108. The data analysis circuit 8108 may comprise one or more of a peak detection circuit, a phase differential circuit, a PLL circuit, a bandpass filter circuit, a frequency transformation circuit, a frequency analysis circuit, a torsional analysis circuit, a bearing analysis circuit, an overload detection circuit, a sensor fault detection circuit, a vibrational resonance circuit for the identification of unfavorable interaction among machines or components, a distortion identification circuit for the identification of unfavorable distortions such as deflections shapes upon operation, overloading of weight, excessive forces, stress and strain-based effects, and the like. The data analysis circuit 8108 may output a component health status as a result of the analysis.

[0634] The data analysis circuit 8108 may determine a state, condition, or status of a component, part, subsystem, or the like of a machine, device, system or item of equipment (collectively referred to herein as a component health status) based on a maximum value of a MUX output for a given input or a rate of change of the value of a MUX output for a given input. The data analysis circuit 8108 may determine a component health status based on a time integration of the value of a MUX for a given input. The data analysis circuit 8108 may determine a component health status based on phase differential of MUX output relative to an on-board time or another sensor. The data analysis circuit 8108 may determine a component health status based on a relationship of value, phase, phase differential, and rate of change for MUX outputs corresponding to one or more input detection values. The data analysis circuit 8108 may determine a component health status based on process stage or component specification or component anticipated state.

[0635] The multiplexer control circuit 8114 may adapt the scheduling of the logical control of the multiplexer based on a component health status, an anticipated component health status, the type of component, the type of equipment being measured, an anticipated state of the equipment, a process stage (different parameters/sensor values) may be important at different stages in a process. The multiplexer control circuit 8114 may adapt the scheduling of the logical control of the multiplexer based on a sequence selected by a user or a remote monitoring application, or on the basis of a user request for a specific value. The multiplexer control circuit 8114 may adapt the scheduling of the logical control of the multiplexer based on the basis of a storage profile or plan (such as based on type and availability of storage elements and parameters as described elsewhere in this disclosure and in the documents incorporated herein by reference), network conditions or availability (also as described elsewhere in this disclosure and in the documents incorporated herein by reference), or value or cost of component or equipment.

[0636] The plurality of sensors 8106 may be wired to ports on the data acquisition circuit 8104. The plurality of sensors 8106 may be wirelessly connected to the data acquisition circuit 8104. The data acquisition circuit 8104 may be able to access detection values corresponding to the output of at least one of the plurality of sensors 8106 where the sensors 8106 may be capturing data on different operational aspects of a piece of equipment or an operating component.

[0637] The selection of the plurality of sensors 8106 for a data monitoring device 8100 designed for a specific component or piece of equipment may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, resolution desired at various positions in a process or plant, reliability of the sensors, and the like. The impact of a failure, time response of a failure (e.g., warning time and/or off-nominal modes occurring before failure), likelihood of failure, and/or sensitivity required, and/or difficulty to detect failure conditions may drive the extent to which a component or piece of equipment is monitored with more sensors, and/or higher capability sensors being dedicated to systems where unexpected or undetected failure would be costly or have severe consequences.

[0638] Depending on the type of equipment, the component being measured, the environment in which the equipment is operating, and the like, sensors 8106 may comprise one or more of, without limitation, a vibration sensor, a thermometer, a hygrometer, a voltage sensor and/or a current sensor (for the component and/or other sensors measuring the component), an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition, and/or spectral analysis, and/or object position or movement), an image sensor, a structured light sensor, a laser-based image sensor, a thermal imager, an acoustic wave sensor, a displacement sensor, a turbidity meter, a viscosity meter, an axial load sensor, a radial load sensor, a tri-axial sensor, an accelerometer, a speedometer, a tachometer, a fluid pressure meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid particle detector, an optical (laser) particle counter, an ultrasonic sensor, an acoustical sensor, a heat flux sensor, a galvanic sensor, a magnetometer, a pH sensor, and the like, including, without limitation, any of the sensors described throughout this disclosure and the documents incorporated by reference.

[0639] The sensors 8106 may provide a stream of data over time that has a phase component, such as relating to acceleration or vibration, allowing for the evaluation of phase or frequency analysis of different operational aspects of a piece of equipment or an operating component. The sensors 8106 may provide a stream of data that is not conventionally phase-based, such as temperature, humidity, load, and the like. The sensors 8106 may provide a continuous or near continuous stream of data over time, periodic readings, event-driven readings, and/or readings according to a selected interval or schedule.

[0640] The sensors 8106 may monitor components such as bearings, sets of bearings, motors, driveshafts, pistons, pumps, conveyors, vibrating conveyors, compressors, drills, and the like in vehicles, oil and gas equipment in the field, in assembly line components, and the like.

[0641] In embodiments, as illustrated in FIG. 51, the sensors 8106 may be part of the data monitoring device 8100, referred to herein in some cases as a data collector, which in some cases may comprise a mobile or portable data collector. In embodiments, as illustrated in FIGS. 52 and 53, one or more external sensors 8126, which are not explicitly part of a monitoring device 8120 but rather are new, previously attached to or integrated into the equipment or component, may be opportunistically connected to, or accessed by the monitoring device 8120. The monitoring device 8120 may include a controller 8122. The controller 8122 may include a data acquisition circuit 8104, a data analysis circuit 8108, a MUX control circuit 8114, and a response circuit 8110. The data acquisition circuit 8104 may comprise a MUX 8112 where the inputs correspond to a subset of the detection values. The MUX control circuit 8114 may be structured to provide the logical control of the MUX and the correspondence of MUX input and detected values based on a subset of the plurality of detection values and/or a command from the response circuit 8110 and/or the output of the data analysis circuit 8108. The data analysis circuit 8108 may comprise one or more of a peak detection circuit, a phase differential circuit, a PLL circuit, a bandpass filter circuit, a frequency transformation circuit, a frequency analysis circuit, a torsional analysis circuit, a bearing analysis circuit, an overload detection circuit, vibrational resonance circuit for the identification of unfavorable interaction among machines or components, a distortion identification circuit for the identification of unfavorable distortions such as deflections shapes upon operation, stress and strain-based effects, and the like.

[0642] The one or more external sensors 8126 may be directly connected to the one or more input ports 8128 on the data acquisition circuit 8104 of the controller 8122 or may be accessed by the data acquisition circuit 8104 wirelessly, such as by a reader, interrogator, or other wireless connection, such as over a short-distance wireless protocol. In embodiments, as shown in FIG. 53, a data acquisition circuit 8104 may further comprise a wireless communication circuit 8130. The data acquisition circuit 8104 may use the wireless communication circuit 8130 to access detection values corresponding to the one or more external sensors 8126 wirelessly or via a separate source or some combination of these methods.

[0643] In embodiments, as illustrated in FIG. 54, the controller 8134 may further comprise a data storage circuit 8136. The data storage circuit 8136 may be structured to store one or more of sensor specifications, component specifications, anticipated state information, detected values, multiplexer output, component models, and the like. The data storage circuit 8136 may provide specifications and anticipated state information to the data analysis circuit 8108.

[0644] In embodiments, the response circuit 8110 may initiate a variety of actions based on the sensor status provided by the data analysis circuit 8108. The response circuit 8110 may adjust a sensor scaling value (e.g., from 100 mV/gram to 10 mV/gram). The response circuit 8110 may select an alternate sensor from a plurality available. The response circuit 8110 may acquire data from a plurality of sensors of different ranges. The response circuit 8110 may recommend an alternate sensor. The response circuit 8110 may issue an alarm or an alert.

[0645] In embodiments, the response circuit 8110 may cause the data acquisition circuit 8104 to enable or disable the processing of detection values corresponding to certain sensors based on the component status. This may include switching to sensors having different response rates, sensitivity, ranges, and the like; accessing new sensors or types of sensors, accessing data from multiple sensors, and the like. Switching may be undertaken based on a model, a set of rules, or the like. In embodiments, switching may be under control of a machine learning system, such that switching is controlled based on one or more metrics of success, combined with input data, over a set of trials, which may occur under supervision of a human supervisor or under control of an automated system. Switching may involve switching from one input port to another (such as to switch from one sensor to another). Switching may involve altering the multiplexing of data, such as combining different streams under different circumstances. Switching may involve activating a system to obtain additional data, such as moving a mobile system (such as a robotic or drone system), to a location where different or additional data is available, such as positioning an image sensor for a different view or positioning a sonar sensor for a different direction of collection, or to a location where different sensors can be accessed, such as moving a collector to connect up to a sensor at a location in an environment by a wired or wireless connection. This switching may be implemented by directing changes to the multiplexer (MUX) control circuit 8114.

[0646] In embodiments, the response circuit 8110 may make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 8110 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like.

[0647] In embodiments, the response circuit 8110 may recommend maintenance at an upcoming process stop or initiate a maintenance call where the maintenance may include the replacement of the sensor with the same or an alternate type of sensor having a different response rate, sensitivity, range, and the like. In embodiments, the response circuit 8110 may implement or recommend process changes—for example to lower the utilization of a component that is near a maintenance interval, operating off-nominally, or failed for purpose but is still at least partially operational, to change the operating speed of a component (such as to put it in a lower-demand mode), to initiate amelioration of an issue (such as to signal for additional lubrication of a roller bearing set, or to signal for an alignment process for a system that is out of balance), and the like.

[0648] In embodiments, the data analysis circuit 8108 and/or the response circuit 8110 may periodically store certain detection values and/or the output of the multiplexers and/or the data corresponding to the logic control of the MUX in the data storage circuit 8136 to enable the tracking of component performance over time. In embodiments, based on sensor status, as described elsewhere herein, recently measured sensor data and related operating conditions such as RPMs, component loads, temperatures, pressures, vibrations, or other sensor data of the types described throughout this disclosure in the data storage circuit 8136 enable the backing out of overloaded/failed sensor data. The signal evaluation circuit 8108 may store data at a higher data rate for greater granularity in future processing, the ability to reprocess at different sampling rates, and/or to enable diagnosing or post-processing of system information where operational data of interest is flagged, and the like.

[0649] In embodiments, as shown in FIGS. 55, 56, 57, and 58, a data monitoring system 8138 may include at least one data monitoring device 8140. The at least one data monitoring device 8140 may include sensors 8106 and a controller 8142 comprising a data acquisition circuit 8104, a data analysis circuit 8108, a data storage circuit 8136, and a communication circuit 8146 to allow data and analysis to be transmitted to a monitoring application 8150 on a remote server 8148. The signal evaluation circuit 8108 may include at least an overload detection circuit (e.g., reference FIGS. 101 and 102) and/or a sensor fault detection circuit (e.g., reference FIGS. 101 and 102). The signal evaluation circuit 8108 may periodically share data with the communication circuit 8146 for transmittal to the remote server 8148 to enable the tracking of component and equipment performance over time and under varying conditions by a monitoring application 8150. Based on the sensor status, the signal evaluation circuit 8108 and/or response circuit 8110 may share data with the communication circuit 8146 for transmittal to the remote server 8148 based on the fit of data relative to one or more criteria. Data may include recent sensor data and additional data such as RPMs, component loads, temperatures, pressures, vibrations, and the like for transmittal. The signal evaluation circuit 8108 may share data at a higher data rate for transmittal to enable greater granularity in processing on the remote server.

[0650] In embodiments, as shown in FIG. 55, the communication circuit 8146 may communicate data directly to a remote server 8148. In embodiments, as shown in FIG. 56, the communication circuit 8146 may communicate data to an intermediate computer 8152 which may include a processor 8154 running an operating system 8156 and a data storage circuit 8158.

[0651] In embodiments as illustrated in FIGS. 57 and 58, a data collection system 8160 may have a plurality of monitoring devices 8144 collecting data on multiple components in a single piece of equipment, collecting data on the same component across a plurality of pieces of equipment, (both the same and different types of equipment) in the same facility, as well as collecting data from monitoring devices in multiple facilities. A monitoring application 8150 on a remote server 8148 may receive and store one or more of detection values, timing signals, and data coming from a plurality of the various monitoring devices 8144.

[0652] In embodiments, as shown in FIG. 57, the communication circuit 8146 may communicate data directly to a remote server 8148. In embodiments, as shown in FIG. 58, the communication circuit 8146 may communicate data to an intermediate computer 8152 which may include a processor 8154 running an operating system 8156 and a data storage circuit 8158. There may be an individual intermediate computer 8152 associated with each monitoring device 8140 or an individual intermediate computer 8152 may be associated with a plurality of monitoring devices 8144 where the intermediate computer 8152 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 8148. Communication to the remote server 8148 may be streaming, batch (e.g., when a connection is available), or opportunistic.

[0653] The monitoring application 8150 may select subsets of the detection values to be jointly analyzed. Subsets for analysis may be selected based on a single type of sensor, component, or a single type of equipment in which a component is operating. Subsets for analysis may be selected or grouped based on common operating conditions such as size of load, operational condition (e.g., intermittent or continuous), operating speed or tachometer output, common ambient environmental conditions such as humidity, temperature, air or fluid particulate, and the like. Subsets for analysis may be selected based on the effects of other nearby equipment such as nearby machines rotating at similar frequencies, nearby equipment producing electromagnetic fields, nearby equipment producing heat, nearby equipment inducing movement or vibration, nearby equipment emitting vapors, chemicals or particulates, or other potentially interfering or intervening effects.

[0654] In embodiments, the monitoring application 8150 may analyze the selected subset. In an example, data from a single sensor may be analyzed over different time periods such as one operating cycle, several operating cycles, a month, a year, the life of the component, or the like. Data from multiple sensors of a common type measuring a common component type may also be analyzed over different time periods. Trends in the data such as changing rates of change associated with start-up or different points in the process may be identified. Correlation of trends and values for different sensors may be analyzed to identify those parameters whose short-term analysis might provide the best prediction regarding expected sensor performance. This information may be transmitted back to the monitoring device to update sensor models, sensor selection, sensor range, sensor scaling, sensor sampling frequency, types of data collected, and the like, and be analyzed locally or to influence the design of future monitoring devices.

[0655] In embodiments, the monitoring application 8150 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for a plurality of sensors, operational history, historical detection values, sensor life models, and the like for use analyzing the selected subset using rule-based or model-based analysis. The monitoring application 8150 may provide recommendations regarding sensor selection, additional data to collect, data to store with sensor data, and the like. The monitoring application 8150 may provide recommendations regarding scheduling repairs and/or maintenance. The monitoring application 8150 may provide recommendations regarding replacing a sensor. The replacement sensor may match the sensor being replaced or the replacement sensor may have a different range, sensitivity, sampling frequency, and the like.

[0656] In embodiments, the monitoring application 8150 may include a remote learning circuit structured to analyze sensor status data (e.g., sensor overload or sensor failure) together with data from other sensors, failure data on components being monitored, equipment being monitored, output being produced, and the like. The remote learning system may identify correlations between sensor overload and data from other sensors.

[0657] An example monitoring system for data collection in an industrial environment includes a data acquisition circuit that interprets a number of detection values, each of the detection values corresponding to input received from at least one of a number of input sensors, a MUX having inputs corresponding to a subset of the detection values, a MUX control circuit that interprets a subset of the number of detection values and provides the logical control of the MUX and the correspondence of MUX input and detected values as a result, where the logic control of the MUX includes adaptive scheduling of the select lines, a data analysis circuit that receives an output from the MUX and data corresponding to the logic control of the MUX resulting in a component health status, an analysis response circuit that performs an operation in response to the component health status, where the number of sensors includes at least two sensors such as a temperature sensor, a load sensor, a vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor, and/or a tachometer. In certain further embodiments, an example system includes: where at least one of the number of detection values may correspond to a fusion of two or more input sensors representing a virtual sensor; where the system further includes a data storage circuit that stores at least one of component specifications and anticipated component state information and buffers a subset of the number of detection values for a predetermined length of time; where the system further includes a data storage circuit that stores at least one of a component specification and anticipated component state information and buffers the output of the MUX and data corresponding to the logic control of the MUX for a predetermined length of time; where the data analysis circuit includes a peak detection circuit, a phase detection circuit, a bandpass filter circuit, a frequency transformation circuit, a frequency analysis circuit, a PLL circuit, a torsional analysis circuit, and/or a bearing analysis circuit; where operation further includes storing additional data in the data storage circuit; where the operation includes at least one of enabling or disabling one or more portions of the MUX circuit; and/or where the operation includes causing the MUX control circuit to alter the logical control of the MUX and the correspondence of MUX input and detected values. In certain embodiments, the system includes at least two multiplexers; control of the correspondence of the multiplexer input and the detected values further includes controlling the connection of the output of a first multiplexer to an input of a second multiplexer; control of the correspondence of the multiplexer input and the detected values further comprises powering down at least a portion of one of the at least two multiplexers; and/or control of the correspondence of MUX input and detected values includes adaptive scheduling of the select lines. In certain embodiments, a data response circuit analyzes the stream of data from one or both MUXes, and recommends an action in response to the analysis.

[0658] An example testing system includes the testing system in communication with a number of analog and digital input sensors, a monitoring device including a data acquisition circuit that interprets a number of detection values, each of the number of detection values corresponding to at least one of the input sensors, a MUX having inputs corresponding to a subset of the detection values, a MUX control circuit that interprets a subset of the number of detection values and provides the logical control of the MUX and control of the correspondence of MUX input and detected values as a result, where the logic control of the MUX includes adaptive scheduling of the select lines, and a user interface enabled to accept scheduling input for select lines and display output of MUX and select line data.

[0659] In embodiments, information about the health or other status or state information of or regarding a component or piece of industrial equipment may be obtained by looking at both the amplitude and phase or timing of data signals relative to related data signals, timers, reference signals or data measurements. An embodiment of a data monitoring device 8500 is shown in FIG. 59 and may include a plurality of sensors 8506 communicatively coupled to a controller 8502. The controller 8502 may include a data acquisition circuit 8504, a signal evaluation circuit 8508 and a response circuit 8510. The plurality of sensors 8506 may be wired to ports on the data acquisition circuit 8504 or wirelessly in communication with the data acquisition circuit 8504. The plurality of sensors 8506 may be wirelessly connected to the data acquisition circuit 8504. The data acquisition circuit 8504 may be able to access detection values corresponding to the output of at least one of the plurality of sensors 8506 where the sensors 8506 may be capturing data on different operational aspects of a piece of equipment or an operating component.

[0660] The selection of the plurality of sensors 8506 for a data monitoring device 8500 designed for a specific component or piece of equipment may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, reliability of the sensors, and the like. The impact of failure may drive the extent to which a component or piece of equipment is monitored with more sensors and/or higher capability sensors being dedicated to systems where unexpected or undetected failure would be costly or have severe consequences.

[0661] Depending on the type of equipment, the component being measured, the environment in which the equipment is operating and the like, sensors 8506 may comprise one or more of, without limitation, a vibration sensor, a thermometer, a hygrometer, a voltage sensor, a current sensor, an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition and/or spectral analysis, and/or object position or movement), an image sensor, a structured light sensor, a laser-based image sensor, an acoustic wave sensor, a displacement sensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axial sensor, an accelerometer, a tachometer, a fluid pressure meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid particle detector, an acoustical sensor, a pH sensor, and the like, including, without limitation, any of the sensors described throughout this disclosure and the documents incorporated by reference.

[0662] The sensors 8506 may provide a stream of data over time that has a phase component, such as relating to acceleration or vibration, allowing for the evaluation of phase or frequency analysis of different operational aspects of a piece of equipment or an operating component. The sensors 8506 may provide a stream of data that is not conventionally phase-based, such as temperature, humidity, load, and the like. The sensors 8506 may provide a continuous or near continuous stream of data over time, periodic readings, event-driven readings, and/or readings according to a selected interval or schedule.

[0663] In embodiments, as illustrated in FIG. 59, the sensors 8506 may be part of the data monitoring device 8500, referred to herein in some cases as a data collector, which in some cases may comprise a mobile or portable data collector. In embodiments, as illustrated in FIGS. 60 and 61, sensors 8518, either new or previously attached to or integrated into the equipment or component, may be opportunistically connected to or accessed by a monitoring device 8512. The sensors 8518 may be directly connected to input ports 8520 on the data acquisition circuit 8516 of a controller 8514 or may be accessed by the data acquisition circuit 8516 wirelessly, such as by a reader, interrogator, or other wireless connection, such as over a short-distance wireless protocol. In embodiments, a data acquisition circuit 8516 may access detection values corresponding to the sensors 8518 wirelessly or via a separate source or some combination of these methods. In embodiments, the data acquisition circuit 8504 may include a wireless communications circuit 8522 able to wirelessly receive data opportunistically from sensors 8518 in the vicinity and route the data to the input ports 8520 on the data acquisition circuit 8516.

[0664] In an embodiment, as illustrated in FIGS. 62 and 63, the signal evaluation circuit 8508 may then process the detection values to obtain information about the component or piece of equipment being monitored. Information extracted by the signal evaluation circuit 8508 may comprise rotational speed, vibrational data including amplitudes, frequencies, phase, and/or acoustical data, and/or non-phase sensor data such as temperature, humidity, image data, and the like.

[0665] The signal evaluation circuit 8508 may include one or more components such as a phase detection circuit 8528 to determine a phase difference between two time-based signals, a phase lock loop circuit 8530 to adjust the relative phase of a signal such that it is aligned with a second signal, timer or reference signal, and/or a band pass filter circuit 8532 which may be used to separate out signals occurring at different frequencies. An example band pass filter circuit 8532 includes any filtering operations understood in the art, including at least a low-pass filter, a high-pass filter, and/or a band pass filter—for example to exclude or reduce frequencies that are not of interest for a particular determination, and/or to enhance the signal for frequencies of interest. Additionally, or alternatively, a band pass filter circuit 8532 includes one or more notch filters or other filtering mechanism to narrow ranges of frequencies (e.g., frequencies from a known source of noise). This may be used to filter out dominant frequency signals such as the overall rotation, and may help enable the evaluation of low amplitude signals at frequencies associated with torsion, bearing failure and the like.

[0666] In embodiments, understanding the relative differences may be enabled by a phase detection circuit 8528 to determine a phase difference between two signals. It may be of value to understand a relative phase offset, if any, between signals such as when a periodic vibration occurs relative to a relative rotation of a piece of equipment. In embodiments, there may be value in understanding where in a cycle shaft vibrations occur relative to a motor control input to better balance the control of the motor. This may be particularly true for systems and components that are operating at relative slow RPMs. Understanding of the phase difference between two signals or between those signals and a timer may enable establishing a relationship between a signal value and where it occurs in a process or rotation. Understanding relative phase differences may help in evaluating the relationship between different components of a system such as in the creation of a vibrational model for an Operational Deflection Shape (ODS).

[0667] The signal evaluation circuit 8508 may perform frequency analysis using techniques such as a digital Fast Fourier transform (FFT), Laplace transform, Z-transform, wavelet transform, other frequency domain transform, or other digital or analog signal analysis techniques, including, without limitation, complex analysis, including complex phase evolution analysis. An overall rotational speed or tachometer may be derived from data from sensors such as rotational velocity meters, accelerometers, displacement meters and the like. Additional frequencies of interest may also be identified. These may include frequencies near the overall rotational speed as well as frequencies higher than that of the rotational speed. These may include frequencies that are nonsynchronous with an overall rotational speed. Signals observed at frequencies that are multiples of the rotational speed may be due to bearing induced vibrations or other behaviors or situations involving bearings. In some instances, these frequencies may be in the range of one times the rotational speed, two times the rotational speed, three times the rotational speed, and the like, up to 3.15 to 15 times the rotational speed, or higher. In some embodiments, the signal evaluation circuit 8508 may select RC components for a band pass filter circuit 8532 based on overall rotational speed to create a band pass filter circuit 8532 to remove signals at expected frequencies such as the overall rotational speed, to facilitate identification of small amplitude signals at other frequencies. In embodiments, variable components may be selected, such that adjustments may be made in keeping with changes in the rotational speed, so that the band pass filter may be a variable band pass filter. This may occur under control of automatically self-adjusting circuit elements, or under control of a processor, including automated control based on a model of the circuit behavior, where a rotational speed indicator or other data is provided as a basis for control.

[0668] In embodiments, rather than performing frequency analysis, the signal evaluation circuit 8508 may utilize the time-based detection values to perform transitory signal analysis. These may include identifying abrupt changes in signal amplitude including changes where the change in amplitude exceeds a predetermined value or exists for a certain duration. In embodiments, the time-based sensor data may be aligned with a timer or reference signal allowing the time-based sensor data to be aligned with, for example, a time or location in a cycle. Additional processing to look at frequency changes over time may include the use of Short-Time Fourier Transforms (STFT) or a wavelet transform.

[0669] In embodiments, frequency-based techniques and time-based techniques may be combined, such as using time-based techniques to determine discrete time periods during which given operational modes or states are occurring and using frequency-based techniques to determine behavior within one or more of the discrete time periods.

[0670] In embodiments, the signal evaluation circuit may utilize demodulation techniques for signals obtained from equipment running at slow speeds such as paper and pulp machines, mining equipment, and the like. A signal evaluation circuit employing a demodulation technique may comprise a band-pass filter circuit, a rectifier circuit, and/or a low pass circuit prior to transforming the data to the frequency domain.

[0671] The response circuit 8510, 8710 may further comprise evaluating the results of the signal evaluation circuit 8508, 8538 and, based on certain criteria, initiating an action. Criteria may include a predetermined maximum or minimum value for a detection value from a specific sensor, a value of a sensor's corresponding detection value over time, a change in value, a rate of change in value, and/or an accumulated value (e.g., a time spent above/below a threshold value, a weighted time spent above/below one or more threshold values, and/or an area of the detected value above/below one or more threshold values). The criteria may include a sensor's detection values at certain frequencies or phases where the frequencies or phases may be based on the equipment geometry, equipment control schemes, system input, historical data, current operating conditions, and/or an anticipated response. The criteria may comprise combinations of data from different sensors such as relative values, relative changes in value, relative rates of change in value, relative values over time, and the like. The relative criteria may change with other data or information such as process stage, type of product being processed, type of equipment, ambient temperature and humidity, external vibrations from other equipment, and the like. The relative criteria may include level of synchronicity with an overall rotational speed, such as to differentiate between vibration induced by bearings and vibrations resulting from the equipment design. In embodiments, the criteria may be reflected in one or more calculated statistics or metrics (including ones generated by further calculations on multiple criteria or statistics), which in turn may be used for processing (such as on board a data collector or by an external system), such as to be provided as an input to one or more of the machine learning capabilities described in this disclosure, to a control system (which may be an on-board data collector or remote, such as to control selection of data inputs, multiplexing of sensor data, storage, or the like), or as a data element that is an input to another system, such as a data stream or data package that may be available to a data marketplace, a SCADA system, a remote control system, a maintenance system, an analytic system, or other system.

[0672] In an illustrative and non-limiting example, an alert may be issued if the vibrational amplitude and/or frequency exceeds a predetermined maximum value, if there is a change or rate of change that exceeds a predetermined acceptable range, and/or if an accumulated value based on vibrational amplitude and/or frequency exceeds a threshold. Certain embodiments are described herein as detected values exceeding thresholds or predetermined values, but detected values may also fall below thresholds or predetermined values—for example where an amount of change in the detected value is expected to occur, but detected values indicate that the change may not have occurred. For example, and without limitation, vibrational data may indicate system agitation levels, properly operating equipment, or the like, and vibrational data below amplitude and/or frequency thresholds may be an indication of a process that is not operating according to expectations. Except where the context clearly indicates otherwise, any description herein describing a determination of a value above a threshold and/or exceeding a predetermined or expected value is understood to include determination of a value below a threshold and/or falling below a predetermined or expected value.

[0673] The predetermined acceptable range may be based on anticipated system response or vibration based on the equipment geometry and control scheme such as number of bearings, relative rotational speed, influx of power to the system at a certain frequency, and the like. The predetermined acceptable range may also be based on long term analysis of detection values across a plurality of similar equipment and components and correlation of data with equipment failure. Based on vibration phase information, a physical location of a problem may be identified. Based on the vibration phase information system design flaws, off-nominal operation, and/or component or process failures may be identified. In some embodiments, an alert may be issued based on changes or rates of change in the data over time such as increasing amplitude or shifts in the frequencies or phases at which a vibration occurs. In some embodiments, an alert may be issued based on accumulated values such as time spent over a threshold, weighted time spent over one or more thresholds, and/or an area of a curve of the detected value over one or more thresholds. In embodiments, an alert may be issued based on a combination of data from different sensors such as relative changes in value, or relative rates of change in amplitude, frequency of phase in addition to values of non-phase sensors such as temperature, humidity and the like. For example, an increase in temperature and energy at certain frequencies may indicate a hot bearing that is starting to fail. In embodiments, the relative criteria for an alarm may change with other data or information such as process stage, type of product being processed on equipment, ambient temperature and humidity, external vibrations from other equipment and the like.

[0674] In embodiments, response circuit 8510 may cause the data acquisition circuit 8504 to enable or disable the processing of detection values corresponding to certain sensors based on the some of the criteria discussed above. This may include switching to sensors having different response rates, sensitivity, ranges, and the like; accessing new sensors or types of sensors, and the like. Switching may be undertaken based on a model, a set of rules, or the like. In embodiments, switching may be under control of a machine learning system, such that switching is controlled based on one or more metrics of success, combined with input data, over a set of trials, which may occur under supervision of a human supervisor or under control of an automated system. Switching may involve switching from one input port to another (such as to switch from one sensor to another). Switching may involve altering the multiplexing of data, such as combining different streams under different circumstances. Switching may involve activating a system to obtain additional data, such as moving a mobile system (such as a robotic or drone system), to a location where different or additional data is available (such as positioning an image sensor for a different view or positioning a sonar sensor for a different direction of collection) or to a location where different sensors can be accessed (such as moving a collector to connect up to a sensor that is disposed at a location in an environment by a wired or wireless connection). The response circuit 8510 may make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 8510 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like.

[0675] In embodiments, the response circuit 8510 may recommend maintenance at an upcoming process stop or initiate a maintenance call. The response circuit 8510 may recommend changes in process or operating parameters to remotely balance the piece of equipment. In embodiments, the response circuit 8510 may implement or recommend process changes—for example to lower the utilization of a component that is near a maintenance interval, operating off-nominally, or failed for purpose but still at least partially operational, to change the operating speed of a component (such as to put it in a lower-demand mode), to initiate amelioration of an issue (such as to signal for additional lubrication of a roller bearing set, or to signal for an alignment process for a system that is out of balance), and the like.

[0676] In embodiments, as shown in FIG. 64, the data monitoring device 8540 may further comprise a data storage circuit 8542, memory, and the like. The signal evaluation circuit 8508 may periodically store certain detection values to enable the tracking of component performance over time.

[0677] In embodiments, based on relevant operating conditions and/or failure modes which may occur in as sensor values approach one or more criteria, the signal evaluation circuit 8508 may store data in the data storage circuit 8542 based on the fit of data relative to one or more criteria, such as those described throughout this disclosure. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 8508 may store additional data such as RPMs, component loads, temperatures, pressures, vibrations or other sensor data of the types described throughout this disclosure. The signal evaluation circuit 8508 may store data at a higher data rate for greater granularity in future processing, the ability to reprocess at different sampling rates, and/or to enable diagnosing or post-processing of system information where operational data of interest is flagged, and the like.

[0678] In embodiments, as shown in FIG. 65, a data monitoring system 8546 may comprise at least one data monitoring device 8548. The at least one data monitoring device 8548 comprising sensors 8506, a controller 8550 comprising a data acquisition circuit 8504, a signal evaluation circuit 8538, a data storage circuit 8542, and a communications circuit 8552 to allow data and analysis to be transmitted to a monitoring application 8556 on a remote server 8554. The signal evaluation circuit 8538 may comprise at least one of a phase detection circuit 8528, a phase lock loop circuit 8530, and/or a band pass circuit 8532. The signal evaluation circuit 8538 may periodically share data with the communication circuit 8552 for transmittal to the remote server 8554 to enable the tracking of component and equipment performance over time and under varying conditions by a monitoring application 8556. Because relevant operating conditions and/or failure modes may occur as sensor values approach one or more criteria, the signal evaluation circuit 8538 may share data with the communication circuit 8552 for transmittal to the remote server 8554 based on the fit of data relative to one or more criteria. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 8538 may share additional data such as RPMs, component loads, temperatures, pressures, vibrations, and the like for transmittal. The signal evaluation circuit 8538 may share data at a higher data rate for transmittal to enable greater granularity in processing on the remote server.

[0679] In embodiments, as illustrated in FIG. 66, a data collection system 8560 may have a plurality of monitoring devices 8558 collecting data on multiple components in a single piece of equipment, collecting data on the same component across a plurality of pieces of equipment (both the same and different types of equipment) in the same facility, as well as collecting data from monitoring devices in multiple facilities. A monitoring application on a remote server may receive and store the data coming from a plurality of the various monitoring devices. The monitoring application may then select subsets of data which may be jointly analyzed. Subsets of monitoring data may be selected based on data from a single type of component or data from a single type of equipment in which the component is operating. Monitoring data may be selected or grouped based on common operating conditions such as size of load, operational condition (e.g., intermittent, continuous), operating speed or tachometer, common ambient environmental conditions such as humidity, temperature, air or fluid particulate, and the like. Monitoring data may be selected based on the effects of other nearby equipment, such as nearby machines rotating at similar frequencies, nearby equipment producing electromagnetic fields, nearby equipment producing heat, nearby equipment inducing movement or vibration, nearby equipment emitting vapors, chemicals or particulates, or other potentially interfering or intervening effects.

[0680] The monitoring application may then analyze the selected data set. For example, data from a single component may be analyzed over different time periods such as one operating cycle, several operating cycles, a month, a year, or the like. Data from multiple components of the same type may also be analyzed over different time periods. Trends in the data such as changes in frequency or amplitude may be correlated with failure and maintenance records associated with the same component or piece of equipment. Trends in the data such as changing rates of change associated with start-up or different points in the process may be identified. Additional data may be introduced into the analysis such as output product quality, output quantity (such as per unit of time), indicated success or failure of a process, and the like. Correlation of trends and values for different types of data may be analyzed to identify those parameters whose short-term analysis might provide the best prediction regarding expected performance. This information may be transmitted back to the monitoring device to update types of data collected and analyzed locally or to influence the design of future monitoring devices.

[0681] In an illustrative and non-limiting example, the monitoring device may be used to collect and process sensor data to measure mechanical torque. The monitoring device may be in communication with or include a high resolution, high speed vibration sensor to collect data over an extended period of time, enough to measure multiple cycles of rotation. For gear driven equipment, the sampling resolution should be such that the number of samples taken per cycle is at least equal to the number of gear teeth driving the component. It will be understood that a lower sampling resolution may also be utilized, which may result in a lower confidence determination and/or taking data over a longer period of time to develop sufficient statistical confidence. This data may then be used in the generation of a phase reference (relative probe) or tachometer signal for a piece of equipment. This phase reference may be used to align phase data such as vibrational data or acceleration data from multiple sensors located at different positions on a component or on different components within a system. This information may facilitate the determination of torque for different components or the generation of an Operational Deflection Shape (ODS), indicating the extent of mechanical deflection of one or more components during an operational mode, which in turn may be used to measure mechanical torque in the component.

[0682] The higher resolution data stream may provide additional data for the detection of transitory signals in low speed operations. The identification of transitory signals may enable the identification of defects in a piece of equipment or component

[0683] In an illustrative and non-limiting example, the monitoring device may be used to identify mechanical jitter for use in failure prediction models. The monitoring device may begin acquiring data when the piece of equipment starts up through ramping up to operating speed and then during operation. Once at operating speed, it is anticipated that the torsional jitter should be minimal and changes in torsion during this phase may be indicative of cracks, bearing faults and the like. Additionally, known torsions may be removed from the signal to facilitate in the identification of unanticipated torsions resulting from system design flaws or component wear. Having phase information associated with the data collected at operating speed may facilitate identification of a location of vibration and potential component wear. Relative phase information for a plurality of sensors located throughout a machine may facilitate the evaluation of torsion as it is propagated through a piece of equipment.

[0684] An example system data collection in an industrial environment includes a data acquisition circuit that interprets a number of detection values from a number of input sensors communicatively coupled to the data acquisition circuit, each of the number of detection values corresponding to at least one of the input sensors, a signal evaluation circuit that obtains at least one of a vibration amplitude, a vibration frequency and a vibration phase location corresponding to at least one of the input sensors in response to the number of detection values, and a response circuit that performs at least one operation in response to at the at least one of the vibration amplitude, the vibration frequency and the vibration phase location. Certain further embodiments of an example system include: where the signal evaluation circuit includes a phase detection circuit, or a phase detection circuit and a phase lock loop circuit and/or a band pass filter; where the number of input sensors includes at least two input sensors providing phase information and at least one input sensor providing non-phase sensor information; the signal evaluation circuit further aligning the phase information provided by the at least two of the input sensors; where the at least one operation is further in response to at least one of: a change in magnitude of the vibration amplitude; a change in frequency or phase of vibration; a rate of change in at least one of vibration amplitude, vibration frequency and vibration phase; a relative change in value between at least two of vibration amplitude, vibration frequency and vibration phase; and/or a relative rate of change between at least two of vibration amplitude, vibration frequency, and vibration phase; the system further including an alert circuit, where the at least one operation includes providing an alert and where the alert may be one of haptic, audible and visual; a data storage circuit, where at least one of the vibration amplitude, vibration frequency, and vibration phase is stored periodically to create a vibration history, and where the at least one operation includes storing additional data in the data storage circuit (e.g., as a vibration fingerprint for a component); where the storing additional data in the data storage circuit is further in response to at least one of: a change in magnitude of the vibration amplitude; a change in frequency or phase of vibration; a rate of change in the vibration amplitude, frequency or phase; a relative change in value between at least two of vibration amplitude, frequency and phase; and a relative rate of change between at least two of vibration amplitude, frequency and phase; the system further comprising at least one of a multiplexing (MUX) circuit whereby alternative combinations of detection values may be selected based on at least one of user input, a detected state, and a selected operating parameter for a machine; where each of the number of detection values corresponds to at least one of the input sensors; where the at least one operation includes enabling or disabling the connection of one or more portions of the multiplexing circuit; a MUX control circuit that interprets a subset of the number of detection values and provides the logical control of the MUX and the correspondence of MUX input and detected values as a result; and/or where the logic control of the MUX includes adaptive scheduling of the select lines.

[0685] An example method of monitoring a component, includes receiving time-based data from at least one sensor, phase-locking the received data with a reference signal, transforming the received time-based data to frequency data, filtering the frequency data to remove tachometer frequencies, identifying low amplitude signals occurring at high frequencies, and activating an alarm if a low amplitude signal exceeds a threshold.

[0686] An example system for data collection, processing, and utilization of signals in an industrial environment includes a plurality of monitoring devices, each monitoring device comprising a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and a vibration phase location corresponding to at least one of the input sensors in response to the corresponding at least one of the plurality of detection values; a data storage facility for storing a subset of the plurality of detection values; a communication circuit structured to communicate at least one selected detection value to a remote server; and a monitoring application on the remote server structured to: receive the at least one selected detection value; jointly analyze a subset of the detection values received from the plurality of monitoring devices; and recommend an action.

[0687] In certain further embodiments, an example system includes: for each monitoring device, the plurality of input sensors include at least one input sensor providing phase information and at least one input sensor providing non-phase input sensor information and where joint analysis includes using the phase information from the plurality of monitoring devices to align the information from the plurality of monitoring devices; where the subset of detection values is selected based on data associated with a detection value including at least one: common type of component, common type of equipment, and common operating conditions and further selected based on one of anticipated life of a component associated with detection values, type of the equipment associated with detection values, and operational conditions under which detection values were measured; and/or where the analysis of the subset of detection values includes feeding a neural net with the subset of detection values and supplemental information to learn to recognize various operating states, health states, life expectancies and fault states utilizing deep learning techniques, wherein the supplemental information comprises one of component specification, component performance, equipment specification, equipment performance, maintenance records, repair records and an anticipated state model.

[0688] An example system for data collection in an industrial environment includes a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to at least one of the input sensors in response to the corresponding at least one of a plurality of detection values; a multiplexing circuit whereby alternative combinations of the detection values may be selected based on at least one of user input, a detected state and a selected operating parameter for a machine, each of the plurality of detection values corresponding to at least one of the input sensors; and a response circuit structured to perform at least one operation in response to at the at least one of the vibration amplitude, vibration frequency and vibration phase location.

[0689] An example system for data collection in a piece of equipment, includes a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a response circuit structured to perform at least one operation in response to at the at least one of the vibration amplitude, vibration frequency and vibration phase location.

[0690] An example system for bearing analysis in an industrial environment, includes a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a life prediction comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value; and a response circuit structured to perform at least one operation in response to at the at least one of the vibration amplitude, vibration frequency and vibration phase location.

[0691] An example motor monitoring system includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the motor and motor components, store historical motor performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a motor analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a motor performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in a motor performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and motor performance parameter.

[0692] An example system for estimating a vehicle steering system performance parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the vehicle steering system, the rack, the pinion, and the steering column, store historical steering system performance and buffer the plurality of detection values for a predetermined length of time;

• • [0693] a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a steering system analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a steering system performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in a steering system performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the steering system performance parameter.

[0694] An example system for estimating a health parameter a pump performance parameter includes a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the pump and pump components associated with the detection values, store historical pump performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a pump analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a pump performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in a pump performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the pump performance parameter, wherein the pump is one of a water pump in a car and a mineral pump.

[0695] An example system for estimating a drill performance parameter for a drilling machine, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the drill and drill components associated with the detection values, store historical drill performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a drill analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a drill performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in a drill performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the drill performance parameter, wherein the drilling machine is one of an oil drilling machine and a gas drilling machine.

[0696] An example system for estimating a conveyor health parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a conveyor and conveyor components associated with the detection values, store historical conveyor performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a conveyor analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a conveyor performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in a conveyor performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the conveyor performance parameter.

[0697] An example system for estimating an agitator health parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for an agitator and agitator components associated with the detection values, store historical agitator performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; an agitator analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in an agitator performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in an agitator performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the agitator performance parameter, wherein the agitator is one of a rotating tank mixer, a large tank mixer, a portable tank mixers, a tote tank mixer, a drum mixer, a mounted mixer and a propeller mixer.

[0698] An example system for estimating a compressor health parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a compressor and compressor components associated with the detection values, store historical compressor performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a compressor analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a compressor performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in a compressor performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the compressor performance parameter.

[0699] An example system for estimating an air conditioner health parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for an air conditioner and air conditioner components associated with the detection values, store historical air conditioner performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; an air conditioner analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in an air conditioner performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in an air conditioner performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the air conditioner performance parameter.

[0700] An example system for estimating a centrifuge health parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a centrifuge and centrifuge components associated with the detection values, store historical centrifuge performance and buffer the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a centrifuge analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a centrifuge performance parameter comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value and analyze the at least one of vibration amplitude, vibration frequency and vibration phase location relative to buffered detection values, specifications and anticipated state information resulting in a centrifuge performance parameter; and a response circuit structured to perform at least one operation in response to at the at least one of vibration amplitude, vibration frequency and vibration phase location and the centrifuge performance parameter.

[0701] In embodiments, information about the health of a component or piece of industrial equipment may be obtained by comparing the values of multiple signals at the same point in a process. This may be accomplished by aligning a signal relative to other related data signals, timers, or reference signals. An embodiment of a data monitoring device 8700, 8718 is shown in FIGS. 67-69 and may include a controller 8702, 8720. The controller may include a data acquisition circuit 8704, 8722, a signal evaluation circuit 8708, a data storage circuit 8716 and an optional response circuit 8710. The signal evaluation circuit 8708 may comprise a timer circuit 8714 and, optionally, a phase detection circuit 8712.

[0702] The data monitoring device may include a plurality of sensors 8706 communicatively coupled to a controller 8702. The plurality of sensors 8706 may be wired to ports on the data acquisition circuit 8704. The plurality of sensors 8706 may be wirelessly connected to the data acquisition circuit 8704 which may be able to access detection values corresponding to the output of at least one of the plurality of sensors 8706 where the sensors 8706 may be capturing data on different operational aspects of a piece of equipment or an operating component. In embodiments, as illustrated in FIGS. 68 and 69, one or more external sensors 8724 which are not explicitly part of a monitoring device 8718 may be opportunistically connected to or accessed by the monitoring device 8718. The data acquisition circuit 8722 may include one or more input ports 8726. The one or more external sensors 8724 may be directly connected to the one or more input ports 8726 on the data acquisition circuit 8722 of the controller 8720. In embodiments, as shown in FIG. 69, a data acquisition circuit 8722 may further comprise a wireless communications circuit 8728 to access detection values corresponding to the one or more external sensors 8724 wirelessly or via a separate source or some combination of these methods.

[0703] The selection of the plurality of sensors 8706 8724 for connection to a data monitoring device 8700 8718 designed for a specific component or piece of equipment may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, resolution desired at various positions in a process or plant, reliability of the sensors, and the like. The impact of a failure, time response of a failure (e.g., warning time and/or off-nominal modes occurring before failure), likelihood of failure, and/or sensitivity required and/or difficulty to detect failed conditions may drive the extent to which a component or piece of equipment is monitored with more sensors and/or higher capability sensors being dedicated to systems where unexpected or undetected failure would be costly or have severe consequences.

[0704] The signal evaluation circuit 8708 may process the detection values to obtain information about a component or piece of equipment being monitored. Information extracted by the signal evaluation circuit 8708 may comprise information regarding what point or time in a process corresponds with a detection value where the point in time is based on a timing signal generated by the timer circuit 8714. The start of the timing signal may be generated by detecting an edge of a control signal such as a rising edge, falling edge or both where the control signal may be associated with the start of a process. The start of the timing signal may be triggered by an initial movement of a component or piece of equipment. The start of the timing signal may be triggered by an initial flow through a pipe or opening or by a flow achieving a predetermined rate. The start of the timing signal may be triggered by a state value indicating a process has commenced—for example the state of a switch, button, data value provided to indicate the process has commenced, or the like. Information extracted may comprise information regarding a difference in phase, determined by the phase detection circuit 8712, between a stream of detection value and the time signal generated by the timer circuit 8714. Information extracted may comprise information regarding a difference in phase between one stream of detection values and a second stream of detection values where the first stream of detection values is used as a basis or trigger for a timing signal generated by the timer circuit.

[0705] Depending on the type of equipment, the component being measured, the environment in which the equipment is operating and the like, sensors 8706 8724 may comprise one or more of, without limitation, a thermometer, a hygrometer, a voltage sensor, a current sensor, an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition and/or spectral analysis, and/or object position or movement), an image sensor, a displacement sensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axial sensor, a tachometer, a fluid pressure meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid particle detector, an acoustical sensor, a pH sensor, and the like.

[0706] The sensors 8706 8724 may provide a stream of data over time that has a phase component, such as acceleration or vibration, allowing for the evaluation of phase or frequency analysis of different operational aspects of a piece of equipment or an operating component. The sensors 8706 8724 may provide a stream of data that is not phase based such as temperature, humidity, load, and the like. The sensors 8706 8724 may provide a continuous or near continuous stream of data over time, periodic readings, event-driven readings, and/or readings according to a selected interval or schedule.

[0707] In embodiments, as illustrated in FIGS. 70 and 71, the data acquisition circuit 8734 may further comprise a multiplexer circuit 8736 as described elsewhere herein. Outputs from the multiplexer circuit 8736 may be utilized by the signal evaluation circuit 8708. The response circuit 8710 may have the ability to turn on and off portions of the multiplexer circuit 8736. The response circuit 8710 may have the ability to control the control channels of the multiplexer circuit 8736.

[0708] The response circuit 8710 may further comprise evaluating the results of the signal evaluation circuit 8708 and, based on certain criteria, initiating an action. The criteria may include a sensor's detection values at certain frequencies or phases relative to the timer signal where the frequencies or phases of interest may be based on the equipment geometry, equipment control schemes, system input, historical data, current operating conditions, and/or an anticipated response. Criteria may include a predetermined maximum or minimum value for a detection value from a specific sensor, a cumulative value of a sensor's corresponding detection value over time, a change in value, a rate of change in value, and/or an accumulated value (e.g., a time spent above/below a threshold value, a weighted time spent above/below one or more threshold values, and/or an area of the detected value above/below one or more threshold values). The criteria may comprise combinations of data from different sensors such as relative values, relative changes in value, relative rates of change in value, relative values over time, and the like. The relative criteria may change with other data or information such as process stage, type of product being processed, type of equipment, ambient temperature and humidity, external vibrations from other equipment, and the like.

[0709] Certain embodiments are described herein as detected values exceeding thresholds or predetermined values, but detected values may also fall below thresholds or predetermined values—for example where an amount of change in the detected value is expected to occur, but detected values indicate that the change may not have occurred. For example, and without limitation, vibrational data may indicate system agitation levels, properly operating equipment, or the like, and vibrational data below amplitude and/or frequency thresholds may be an indication of a process that is not operating according to expectations. Except where the context clearly indicates otherwise, any description herein describing a determination of a value above a threshold and/or exceeding a predetermined or expected value is understood to include determination of a value below a threshold and/or falling below a predetermined or expected value.

[0710] The predetermined acceptable range may be based on anticipated system response or vibration based on the equipment geometry and control scheme such as number of bearings, relative rotational speed, influx of power to the system at a certain frequency, and the like. The predetermined acceptable range may also be based on long term analysis of detection values across a plurality of similar equipment and components and correlation of data with equipment failure.

[0711] In some embodiments, an alert may be issued based on the some of the criteria discussed above. In an illustrative example, an increase in temperature and energy at certain frequencies may indicate a hot bearing that is starting to fail. In embodiments, the relative criteria for an alarm may change with other data or information such as process stage, type of product being processed on equipment, ambient temperature and humidity, external vibrations from other equipment and the like. In an illustrative and non-limiting example, the response circuit 8710 may initiate an alert if a vibrational amplitude and/or frequency exceeds a predetermined maximum value, if there is a change or rate of change that exceeds a predetermined acceptable range, and/or if an accumulated value based on vibrational amplitude and/or frequency exceeds a threshold.

[0712] In embodiments, response circuit 8710 may cause the data acquisition circuit 8704 to enable or disable the processing of detection values corresponding to certain sensors based on the some of the criteria discussed above. This may include switching to sensors having different response rates, sensitivity, ranges, and the like; accessing new sensors or types of sensors, and the like. This switching may be implemented by changing the control signals for a multiplexer circuit 8736 and/or by turning on or off certain input sections of the multiplexer circuit 8736. The response circuit 8710 may make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 8710 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like.

[0713] In embodiments, the response circuit 8710 may recommend maintenance at an upcoming process stop or initiate a maintenance call. The response circuit 8710 may recommend changes in process or operating parameters to remotely balance the piece of equipment. In embodiments, the response circuit 8710 may implement or recommend process changes—for example to lower the utilization of a component that is near a maintenance interval, operating off-nominally, or failed for purpose but still at least partially operational. In an illustrative example, vibration phase information, derived by the phase detection circuit 8712 relative to a timer signal from the timer circuit 8714, may be indicative of a physical location of a problem. Based on the vibration phase information, system design flaws, off-nominal operation, and/or component or process failures may be identified.

[0714] In embodiments, based on relevant operating conditions and/or failure modes which may occur in as sensor values approach one or more criteria, the signal evaluation circuit 8708 may store data in the data storage circuit 8716 based on the fit of data relative to one or more criteria. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 8708 may store additional data such as RPMs, component loads, temperatures, pressures, vibrations in the data storage circuit 8716. The signal evaluation circuit 8708 may store data at a higher data rate for greater granularity in future processing, the ability to reprocess at different sampling rates, and/or to enable diagnosing or post-processing of system information where operational data of interest is flagged, and the like.

[0715] In embodiments, as shown in FIGS. 72 and 73 and 74 and 75, a data monitoring system 8762 may include at least one data monitoring device 8768. The at least one data monitoring device 8768 may include sensors 8706 and a controller 8770 comprising a data acquisition circuit 8704, a signal evaluation circuit 8772, a data storage circuit 8742, and a communications circuit 8752 to allow data and analysis to be transmitted to a monitoring application 8776 on a remote server 8774. The signal evaluation circuit 8772 may include at least one of a phase detection circuit 8712 and a timer circuit 8714. The signal evaluation circuit 8772 may periodically share data with the communication circuit 8752 for transmittal to the remote server 8774 to enable the tracking of component and equipment performance over time and under varying conditions by a monitoring application 8776. Because relevant operating conditions and/or failure modes may occur as sensor values approach one or more criteria, the signal evaluation circuit 8708 may share data with the communication circuit 8752 for transmittal to the remote server 8774 based on the fit of data relative to one or more criteria. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 8708 may share additional data such as RPMs, component loads, temperatures, pressures, vibrations, and the like for transmittal. The signal evaluation circuit 8772 may share data at a higher data rate for transmittal to enable greater granularity in processing on the remote server.

[0716] In embodiments, as shown in FIG. 72, the communications circuit 8752 may communicated data directly to a remote server 8774. In embodiments, as shown in FIG. 73, the communications circuit 8752 may communicate data to an intermediate computer 8754 which may include a processor 8756 running an operating system 8758 and a data storage circuit 8760. The intermediate computer 8754 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 8774.

[0717] In embodiments as illustrated in FIGS. 74 and 75, a data collection system 8762 may have a plurality of monitoring devices 8768 collecting data on multiple components in a single piece of equipment, collecting data on the same component across a plurality of pieces of equipment, (both the same and different types of equipment) in the same facility as well as collecting data from monitoring devices in multiple facilities. In embodiments, as shown in FIG. 74 the communications circuit 8752 may communicated data directly to a remote server 8774. In embodiments, as shown in FIG. 75, the communications circuit 8752 may communicate data to an intermediate computer 8754 which may include a processor 8756 running an operating system 8758 and a data storage circuit 8760. The intermediate computer 8754 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 8774.

[0718] In embodiments, a monitoring application 8776 on a remote server 8774 may receive and store one or more of detection values, timing signals and data coming from a plurality of the various monitoring devices 8768. The monitoring application 8776 may then select subsets of the detection values, timing signals and data to be jointly analyzed. Subsets for analysis may be selected based on a single type of component or a single type of equipment in which a component is operating. Subsets for analysis may be selected or grouped based on common operating conditions such as size of load, operational condition (e.g., intermittent, continuous, process stage), operating speed or tachometer, common ambient environmental conditions such as humidity, temperature, air or fluid particulate, and the like. Subsets for analysis may be selected based on the effects of other nearby equipment such as nearby machines rotating at similar frequencies.

[0719] The monitoring application 8776 may then analyze the selected subset. In an illustrative example, data from a single component may be analyzed over different time periods such as one operating cycle, several operating cycles, a month, a year, the life of the component or the like. Data from multiple components of the same type may also be analyzed over different time periods. Trends in the data such as changes in frequency or amplitude may be correlated with failure and maintenance records associated with the same or a related component or piece of equipment. Trends in the data such as changing rates of change associated with start-up or different points in the process may be identified. Additional data may be introduced into the analysis such as output product quality, indicated success or failure of a process, and the like. Correlation of trends and values for different types of data may be analyzed to identify those parameters whose short-term analysis might provide the best prediction regarding expected performance. This information may be transmitted back to the monitoring device to update types of data collected and analyzed locally or to influence the design of future monitoring devices.

[0720] In an illustrative and non-limiting example, a monitoring device 8768 may be used to collect and process sensor data to measure mechanical torque. The monitoring device 8768 may be in communication with or include a high resolution, high speed vibration sensor to collect data over a period of time sufficient to measure multiple cycles of rotation. For gear driven components, the sampling resolution of the sensor should be such that the number of samples taken per cycle is at least equal to the number of gear teeth driving the component. It will be understood that a lower sampling resolution may also be utilized, which may result in a lower confidence determination and/or taking data over a longer period of time to develop sufficient statistical confidence. This data may then be used in the generation of a phase reference (relative probe) or tachometer signal for a piece of equipment. This phase reference may be used directly or used by the timer circuit 8714 to generate a timing signal to align phase data such as vibrational data or acceleration data from multiple sensors located at different positions on a component or on different components within a system. This information may facilitate the determination of torque for different components or the generation of an Operational Deflection Shape (ODS).

[0721] A higher resolution data stream may also provide additional data for the detection of transitory signals in low speed operations. The identification of transitory signals may enable the identification of defects in a piece of equipment or component operating a low RPMs.

[0722] In an illustrative and non-limiting example, the monitoring device may be used to identify mechanical jitter for use in failure prediction models. The monitoring device may begin acquiring data when the piece of equipment starts up, through ramping up to operating speed, and then during operation. Once at operating speed, it is anticipated that the torsional jitter should be minimal or within expected ranges, and changes in torsion during this phase may be indicative of cracks, bearing faults, and the like. Additionally, known torsions may be removed from the signal to facilitate in the identification of unanticipated torsions resulting from system design flaws, component wear, or unexpected process events. Having phase information associated with the data collected at operating speed may facilitate identification of a location of vibration and potential component wear, and/or may be further correlated to a type of failure for a component. Relative phase information for a plurality of sensors located throughout a machine may facilitate the evaluation of torsion as it is propagated through a piece of equipment.

[0723] In embodiments, the monitoring application 8776 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for plurality of component types, operational history, historical detection values, component life models, and the like for use in analyzing the selected subset using rule-based or model-based analysis. In embodiments, the monitoring application 8776 may feed a neural net with the selected subset to learn to recognize various operating state, health states (e.g., lifetime predictions) and fault states utilizing deep learning techniques. In embodiments, a hybrid of the two techniques (model-based learning and deep learning) may be used.

[0724] In an illustrative and non-limiting example, component health of: conveyors and lifters in an assembly line; water pumps on industrial vehicles; factory air conditioning units; drilling machines, screw drivers, compressors, pumps, gearboxes, vibrating conveyors, mixers and motors situated in the oil and gas fields; factory mineral pumps; centrifuges, and refining tanks situated in oil and gas refineries; and compressors in gas handling systems may be monitored using the phase detection and alignment techniques, data monitoring devices and data collection systems described herein.

[0725] In an illustrative and non-limiting example, the component health of equipment to promote chemical reactions deployed in chemical and pharmaceutical production lines (e.g. rotating tank/mixer agitators, mechanical/rotating agitators, and propeller agitators) may be evaluated using the phase detection and alignment techniques, data monitoring devices and data collection systems described herein.

[0726] In an illustrative and non-limiting example, the component health of vehicle steering mechanisms and/or vehicle engines may be evaluated using the phase detection and alignment techniques, data monitoring devices and data collection systems described herein.

[0727] An example monitoring system for data collection, includes a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a signal evaluation circuit comprising: a timer circuit structured to generate at least one timing signal; and a phase detection circuit structured to determine a relative phase difference between at least one of the plurality of detection values and at least one of the timing signals from the timer circuit; and a response circuit structured to perform at least one operation in response to the relative phase difference. In certain further embodiments, an example system includes:

• • [0728] wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; a data storage circuit, wherein the relative phase difference and at least one of the detection values and the timing signal are stored; wherein the at least one operation further comprises storing additional data in the data storage circuit; wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference; wherein the data acquisition circuit further comprises at least one multiplexer circuit (MUX) whereby alternative combinations of detection values may be selected based on at least one of user input and a selected operating parameter for a machine, wherein each of the plurality of detection values corresponds to at least one of the input sensors; wherein the at least one operation comprises enabling or disabling one or more portions of the multiplexer circuit, or altering the multiplexer control lines; wherein the data acquisition circuit comprises at least two multiplexer circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits; and/or the system further comprising a MUX control circuit structured to interpret a subset of the plurality of detection values and provide the logical control of the MUX and the correspondence of MUX input and detected values as a result, wherein the logic control of the MUX comprises adaptive scheduling of the select lines.

[0729] An example system for data collection, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a signal evaluation circuit comprising: a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; and a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a phase response circuit structured to perform at least one operation in response to the phase difference. In certain further embodiments, an example system includes wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one the plurality of detection values and a relative rate of change in amplitude and relative phase of at least one the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; where the system, further includes a data storage circuit; wherein the relative phase difference and at least one of the detection values and the timing signal are stored; wherein the at least one operation further includes storing additional data in the data storage circuit; wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference; wherein the data acquisition circuit further includes at least one multiplexer (MUX) circuit whereby alternative combinations of detection values may be selected based on at least one of user input and a selected operating parameter for a machine; wherein each of the plurality of detection values corresponds to at least one of the input sensors; wherein the at least one operation comprises enabling or disabling one or more portions of the multiplexer circuit, or altering the multiplexer control lines; wherein the data acquisition circuit comprises at least two multiplexer circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits; where the system further comprising a MUX control circuit structured to interpret a subset of the plurality of detection values and provide the logical control of the MUX and the correspondence of MUX input and detected values as a result; and/or wherein the logic control of the MUX comprises adaptive scheduling of the select lines.

[0730] An example system for data collection, processing, and utilization of signals in an industrial environment includes a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a signal evaluation circuit comprising: a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; and a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; a data storage facility for storing a subset of the plurality of detection values and the timing signal; a communication circuit structured to communicate at least one selected detection value and the timing signal to a remote server; and a monitoring application on the remote server structured to receive the at least one selected detection value and the timing signal; jointly analyze a subset of the detection values received from the plurality of monitoring devices; and recommend an action. In certain embodiments, the example system further includes wherein joint analysis comprises using the timing signal from each of the plurality of monitoring devices to align the detection values from the plurality of monitoring devices and/or wherein the subset of detection values is selected based on data associated with a detection value comprising at least one: common type of component, common type of equipment, and common operating conditions.

[0731] An example system for data collection in an industrial environment, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit, the data acquisition circuit comprising a multiplexer circuit whereby alternative combinations of the detection values may be selected based on at least one of user input, a detected state and a selected operating parameter for a machine, each of the plurality of detection values corresponding to at least one of the input sensors; a signal evaluation circuit comprising: a timer circuit structured to generate a timing signal; and a phase detection circuit structured to determine a relative phase difference between at least one of the plurality of detection values and a signal from the timer circuit; and a response circuit structured to perform at least one operation in response to the phase difference.

[0732] An example monitoring system for data collection in a piece of equipment, includes a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; and a response circuit structured to perform at least one operation in response to at the at least one of the vibration amplitude, vibration frequency and vibration phase location.

[0733] A monitoring system forbearing analysis in an industrial environment, the monitoring device includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a timer circuit structured to generate a timing signal a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time; a timer circuit structured to generate a timing signal based on a first detected value of the plurality of detection values; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a life prediction comprising: a phase detection circuit structured to determine a relative phase difference between a second detection value of the plurality of detection values and the timing signal; a signal evaluation circuit structured to obtain at least one of vibration amplitude, vibration frequency and vibration phase location corresponding to a second detected value; and a response circuit structured to perform at least one operation in response to at the at least one of the vibration amplitude, vibration frequency and vibration phase location.

[0734] In embodiments, information about the health or other status or state information of or regarding a component or piece of industrial equipment may be obtained by monitoring the condition of various components throughout a process. Monitoring may include monitoring the amplitude of a sensor signal measuring attributes such as temperature, humidity, acceleration, displacement and the like. An embodiment of a data monitoring device 9000 is shown in FIG. 76 and may include a plurality of sensors 9006 communicatively coupled to a controller 9002. The controller 9002, which may be part of a data collection device, such as a mobile data collector, or part of a system, such as a network-deployed or cloud-deployed system, may include a data acquisition circuit 9004, a signal evaluation circuit 9008 and a response circuit 9010. The signal evaluation circuit 9008 may comprise a peak detection circuit 9012. Additionally, the signal evaluation circuit 9008 may optionally comprise one or more of a phase detection circuit 9016, a bandpass filter circuit 9018, a phase lock loop circuit, a torsional analysis circuit, a bearing analysis circuit, and the like. The bandpass filter 9018 may be used to filter a stream of detection values such that values, such as peaks and valleys, are detected only at or within bands of interest, such as frequencies of interest. The data acquisition circuit 9004 may include one or more analog-to-digital converter circuits 9014. A peak amplitude detected by the peak detection circuit 9012 may be input into one or more analog-to-digital converter circuits 9014 to provide a reference value for scaling output of the analog-to-digital converter circuits 9014 appropriately.

[0735] The plurality of sensors 9006 may be wired to ports on the data acquisition circuit 9004. The plurality of sensors 9006 may be wirelessly connected to the data acquisition circuit 9004. The data acquisition circuit 9004 may be able to access detection values corresponding to the output of at least one of the plurality of sensors 9006 where the sensors 9006 may be capturing data on different operational aspects of a piece of equipment or an operating component.

[0736] The selection of the plurality of sensors 9006 for a data monitoring device 9000 designed for a specific component or piece of equipment may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, resolution desired at various positions in a process or plant, reliability of the sensors, power availability, power utilization, storage utilization, and the like. The impact of a failure, time response of a failure (e.g., warning time and/or off-optimal modes occurring before failure), likelihood of failure, extent of impact of failure, and/or sensitivity required and/or difficulty to detection failure conditions may drive the extent to which a component or piece of equipment is monitored with more sensors and/or higher capability sensors being dedicated to systems where unexpected or undetected failure would be costly or have severe consequences.

[0737] The signal evaluation circuit 9008 may process the detection values to obtain information about a component or piece of equipment being monitored. Information extracted by the signal evaluation circuit 9008 may comprise information regarding a peak value of a signal such as a peak temperature, peak acceleration, peak velocity, peak pressure, peak weight bearing, peak strain, peak bending, or peak displacement. The peak detection may be done using analog or digital circuits. In embodiments, the peak detection circuit 9012 may be able to distinguish between “local” or short term peaks in a stream of detection values and a “global” or longer term peak. In embodiments, the peak detection circuit 9012 may be able to identify peak shapes (not just a single peak value) such as flat tops, asymptotic approaches, discrete jumps in the peak value or rapid/steep climbs in peak value, sinusoidal behavior within ranges and the like. Flat topped peaks may indicate saturation at of a sensor. Asymptotic approaches to a peak may indicate linear system behavior. Discrete jumps in value or steep changes in peak value may indicate quantized or nonlinear behavior of either the sensor doing the measurement or the behavior of the component. In embodiments, the system may be able to identify sinusoidal variations in the peak value within an envelope, such as an envelope established by line or curve connecting a series of peak values. It should be noted that references to “peaks” should be understood to encompass one or more “valleys,” representing a series of low points in measurement, except where context indicates otherwise.

[0738] In embodiments, a peak value may be used as a reference for an analog-to-digital conversion circuit 9014.

[0739] In an illustrative and non-limiting example, a temperature probe may measure the temperature of a gear as it rotates in a machine. The peak temperature may be detected by a peak detection circuit 9012. The peak temperature may be fed into an analog-to-digital converter circuit 9014 to appropriately scale a stream of detection values corresponding to temperature readings of the gear as it rotates in a machine. The phase of the stream of detection values corresponding to temperature relative to an orientation of the gear may be determined by the phase detection circuit 9016. Knowing where in the rotation of the gear a peak temperature is occurring may allow the identification of a bad gear tooth.

[0740] In some embodiments, two or more sets of detection values may be fused to create detection values for a virtual sensor. A peak detection circuit may be used to verify consistency in timing of peak values between at least one of the two or more sets of detection values and the detection values for the virtual sensor.

[0741] In embodiments, the signal evaluation circuit 9008 may be able to reset the peak detection circuit 9012 upon start-up of the monitoring device 9000, upon edge detection of a control signal of the system being monitored, based on a user input, after a system error and the like. In embodiments, the signal evaluation circuit 9008 may discard an initial portion of the output of the peak detection circuit 9012 prior to using the peak value as a reference value for an analog-to-digital conversion circuit to allow the system to fully come on line.

[0742] Depending on the type of equipment, the component being measured, the environment in which the equipment is operating and the like, sensors 9006 may comprise one or more of, without limitation, a vibration sensor, a thermometer, a hygrometer, a voltage sensor, a current sensor, an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition and/or spectral analysis, and/or object position or movement), an image sensor, a structured light sensor, a laser-based image sensor, an acoustic wave sensor, a displacement sensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axial sensor, an accelerometer, a tachometer, a fluid pressure meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid particle detector, an acoustical sensor, a pH sensor, and the like, including, without limitation, any of the sensors described throughout this disclosure and the documents incorporated by reference.

[0743] The sensors 9006 may provide a stream of data over time that has a phase component, such as relating to acceleration or vibration, allowing for the evaluation of phase or frequency analysis of different operational aspects of a piece of equipment or an operating component. The sensors 9006 may provide a stream of data that is not conventionally phase-based, such as temperature, humidity, load, and the like. The sensors 9006 may provide a continuous or near continuous stream of data over time, periodic readings, event-driven readings, and/or readings according to a selected interval or schedule.

[0744] In embodiments, as illustrated in FIG. 76, the sensors 9006 may be part of the data monitoring device 9000, referred to herein in some cases as a data collector, which in some cases may comprise a mobile or portable data collector. In embodiments, as illustrated in FIGS. 77 and 78, one or more external sensors 9026, which are not explicitly part of a monitoring device 9020 but rather are new, previously attached to or integrated into the equipment or component, may be opportunistically connected to or accessed by the monitoring device 9020. The monitoring device 9020 may include a controller 9022. The controller 9022 may include a response circuit 9010, a signal evaluation circuit 9008 and a data acquisition circuit 9004. The signal evaluation circuit 9008 may include a peak detection circuit 9012 and optionally a phase detection circuit 9016 and/or a bandpass filter circuit 9018. The data acquisition circuit 9004 may include one or more input ports 9028. The one or more external sensors 9026 may be directly connected to the one or more input ports 9028 on the data acquisition circuit 9004 of the controller 9022 or may be accessed by the data acquisition circuit 9004 wirelessly, such as by a reader, interrogator, or other wireless connection, such as over a short-distance wireless protocol. In embodiments as shown in FIG. 78, a data acquisition circuit 9004 may further comprise a wireless communication circuit 9030. The data acquisition circuit 9004 may use the wireless communication circuit 9030 to access detection values corresponding to the one or more external sensors 9026 wirelessly or via a separate source or some combination of these methods.

[0745] In embodiments as illustrated in FIG. 79, the data acquisition circuit 9036 may further comprise a multiplexer circuit 9038 as described elsewhere herein. Outputs from the multiplexer circuit 9038 may be utilized by the signal evaluation circuit 9008. The response circuit 9010 may have the ability to turn on and off portions of the multiplexor circuit 9038. The response circuit 9010 may have the ability to control the control channels of the multiplexor circuit 9038.

[0746] The response circuit 9010 may evaluate the results of the signal evaluation circuit 9008 and, based on certain criteria, initiate an action. The criteria may include a predetermined peak value for a detection value from a specific sensor, a cumulative value of a sensor's corresponding detection value over time, a change in peak value, a rate of change in a peak value, and/or an accumulated value (e.g., a time spent above/below a threshold value, a weighted time spent above/below one or more threshold values, and/or an area of the detected value above/below one or more threshold values). The criteria may comprise combinations of data from different sensors such as relative values, relative changes in value, relative rates of change in value, relative values over time, and the like. The relative criteria may change with other data or information such as process stage, type of product being processed, type of equipment, ambient temperature and humidity, external vibrations from other equipment, and the like. The relative criteria may be reflected in one or more calculated statistics or metrics (including ones generated by further calculations on multiple criteria or statistics), which in turn may be used for processing (such as an on-board a data collector or by an external system), such as to be provided as an input to one or more of the machine learning capabilities described in this disclosure, to a control system (which may be on-board a data collector or remote, such as to control selection of data inputs, multiplexing of sensor data, storage, or the like), or as a data element that is an input to another system, such as a data stream or data package that may be available to a data marketplace, a SCADA system, a remote control system, a maintenance system, an analytic system, or other system.

[0747] Certain embodiments are described herein as detected values exceeding thresholds or predetermined values, but detected values may also fall below thresholds or predetermined values—for example where an amount of change in the detected value is expected to occur, but detected values indicate that the change may not have occurred. For example, and without limitation, vibrational data may indicate system agitation levels, properly operating equipment, or the like, and vibrational data below amplitude and/or frequency thresholds may be an indication of a process that is not operating according to expectations. For example, in a process involving a blender, a mixer, an agitator or the like, the absence of vibration may indicate that a blade, fin, vane or other working element is unable to move adequately, such as, for example, as a result of a working material being excessively viscous or as a result of a problem in gears (e.g., stripped gears, seizing in gears, or the like (a clutch, or the like). Except where the context clearly indicates otherwise, any description herein describing a determination of a value above a threshold and/or exceeding a predetermined or expected value is understood to include determination of a value below a threshold and/or falling below a predetermined or expected value.

[0748] The predetermined acceptable range may be based on anticipated system response or vibration based on the equipment geometry and control scheme such as number of bearings, relative rotational speed, influx of power to the system at a certain frequency, and the like. The predetermined acceptable range may also be based on long term analysis of detection values across a plurality of similar equipment and components and correlation of data with equipment failure.

[0749] In embodiments, the response circuit 9010 may issue an alert based on one or more of the criteria discussed above. In an illustrative example, an increase in peak temperature beyond a predetermined value may indicate a hot bearing that is starting to fail. In embodiments, the relative criteria for an alarm may change with other data or information such as process stage, type of product being processed on equipment, ambient temperature and humidity, external vibrations from other equipment and the like. In an illustrative and non-limiting example, the response circuit 9010 may initiate an alert if an amplitude, such as a vibrational amplitude and/or frequency, exceeds a predetermined maximum value, if there is a change or rate of change that exceeds a predetermined acceptable range, and/or if an accumulated value based on such amplitude and/or frequency exceeds a threshold.

[0750] In embodiments, the response circuit 9010 may cause the data acquisition circuit 9004 to enable or disable the processing of detection values corresponding to certain sensors based on one or more of the criteria discussed above. This may include switching to sensors having different response rates, sensitivity, ranges, and the like; accessing new sensors or types of sensors, accessing data from multiple sensors, and the like. Switching may be based on a detected peak value for the sensor being switched or based on the peak value of another sensor. Switching may be undertaken based on a model, a set of rules, or the like. In embodiments, switching may be under control of a machine learning system, such that switching is controlled based on one or more metrics of success, combined with input data, over a set of trials, which may occur under supervision of a human supervisor or under control of an automated system. Switching may involve switching from one input port to another (such as to switch from one sensor to another). Switching may involve altering the multiplexing of data, such as combining different streams under different circumstances. Switching may involve activating a system to obtain additional data, such as moving a mobile system (such as a robotic or drone system), to a location where different or additional data is available (such as positioning an image sensor for a different view or positioning a sonar sensor for a different direction of collection) or to a location where different sensors can be accessed (such as moving a collector to connect up to a sensor that is disposed at a location in an environment by a wired or wireless connection). This switching may be implemented by changing the control signals for a multiplexor circuit 9038 and/or by turning on or off certain input sections of the multiplexor circuit 9038.

[0751] In embodiments, the response circuit 9010 may adjust a sensor scaling value using the detected peak as a reference voltage. The response circuit 9010 may adjust a sensor sampling rate such that the peak value is captured.

[0752] The response circuit 9010 may identify sensor overload. In embodiments, the response circuit 9010 may make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 9010 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like.

[0753] In embodiments, the response circuit 9010 may recommend maintenance at an upcoming process stop or initiate a maintenance call where the maintenance may include the replacement of the sensor with the same or an alternate type of sensor having a different response rate, sensitivity, range and the like. In embodiments, the response circuit 9010 may implement or recommend process changes—for example, to lower the utilization of a component that is near a maintenance interval, operating off-nominally, or failed for purpose but still at least partially operational, to change the operating speed of a component (such as to put it in a lower-demand mode), to initiate amelioration of an issue (such as to signal for additional lubrication of a roller bearing set, or to signal for an alignment process for a system that is out of balance), and the like.

[0754] In embodiments, as shown in FIG. 80, the data monitoring device 9040 may include sensors 9006 and a controller 9042 which may include a data acquisition circuit 9004, and a signal evaluation circuit 9008. The signal evaluation circuit 9008 may include a peak detection circuit 9012 and, optionally, a phased detection circuit 9016 and/or a bandpass filter circuit 9018. The controller 9042 may further include a data storage circuit 9044, memory, and the like. The controller 9042 may further include a response circuit 9010. The signal evaluation circuit 9008 may periodically store certain detection values in the data storage circuit 9044 to enable the tracking of component performance over time.

[0755] In embodiments, based on relevant criteria as described elsewhere herein, operating conditions and/or failure modes which may occur as sensor values approach one or more criteria, the signal evaluation circuit 9008 may store data in the data storage circuit 9044 based on the fit of data relative to one or more criteria, such as those described throughout this disclosure. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 9008 may store additional data such as RPMs, component loads, temperatures, pressures, vibrations or other sensor data of the types described throughout this disclosure in the data storage circuit 9044. The signal evaluation circuit 9008 may store data at a higher data rate for greater granularity in future processing, the ability to reprocess at different sampling rates, and/or to enable diagnosing or post-processing of system information where operational data of interest is flagged, and the like.

[0756] In embodiments, the signal evaluation circuit 9008 may store new peaks that indicate changes in overall scaling over a long duration (e.g., scaling a data stream based on historical peaks over months of analysis). The signal evaluation circuit 9008 may store data when historical peak values are approached (e.g., as temperatures, pressures, vibrations, velocities, accelerations and the like approach historical peaks).

[0757] In embodiments as shown in FIGS. 81 and 82 and 83 and 84, a data monitoring system 9046 may include at least one data monitoring device 9048. At least one data monitoring device 9048 may include sensors 9006 and a controller 9050 comprising a data acquisition circuit 9004, a signal evaluation circuit 9008, a data storage circuit 9044, and a communication circuit 9052 to allow data and analysis to be transmitted to a monitoring application 9056 on a remote server 9054. The signal evaluation circuit 9008 may include at least one of a peak detection circuit 9012. The signal evaluation circuit 9008 may periodically share data with the communication circuit 9052 for transmittal to the remote server 9054 to enable the tracking of component and equipment performance over time and under varying conditions by a monitoring application 9056. Because relevant operating conditions and/or failure modes may occur as sensor values approach one or more criteria as described elsewhere herein, the signal evaluation circuit 9008 may share data with the communication circuit 9052 for transmittal to the remote server 9054 based on the fit of data relative to one or more criteria. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 9008 may share additional data such as RPMs, component loads, temperatures, pressures, vibrations, and the like for transmittal. The signal evaluation circuit 9008 may share data at a higher data rate for transmittal to enable greater granularity in processing on the remote server.

[0758] In embodiments, as shown in FIG. 81, the communication circuit 9052 may communicate data directly to a remote server 9054. In embodiments, as shown in FIG. 82, the communication circuit 9052 may communicate data to an intermediate computer 9058 which may include a processor 9060 running an operating system 9062 and a data storage circuit 9064.

[0759] In embodiments, as illustrated in FIGS. 83 and 84, a data collection system 9066 may have a plurality of monitoring devices 9048 collecting data on multiple components in a single piece of equipment, collecting data on the same component across a plurality of pieces of equipment (both the same and different types of equipment) in the same facility as well as collecting data from monitoring devices in multiple facilities. A monitoring application 9056 on a remote server 9054 may receive and store one or more of detection values, timing signals or data coming from a plurality of the various monitoring devices 9048.

[0760] In embodiments, as shown in FIG. 81, the communication circuit 9052 may communicate data directly to a remote server 9054. In embodiments, as shown in FIG. 82, the communication circuit 9052 may communicate data to an intermediate computer 9058 which may include a processor 9060 running an operating system 9062 and a data storage circuit 9064. There may be an individual intermediate computer 9058 associated with each monitoring device 9048 or an individual intermediate computer 9058 may be associated with a plurality of monitoring devices 9048 where the intermediate computer 9058 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 9054.

[0761] The monitoring application 9056 may select subsets of the detection values, timing signals and data to be jointly analyzed. Subsets for analysis may be selected based on a single type of component or a single type of equipment in which a component is operating. Subsets for analysis may be selected or grouped based on common operating conditions such as size of load, operational condition (e.g., intermittent, continuous), operating speed or tachometer, common ambient environmental conditions such as humidity, temperature, air or fluid particulate, and the like. Subsets for analysis may be selected based on the effects of other nearby equipment such as nearby machines rotating at similar frequencies, nearby equipment producing electromagnetic fields, nearby equipment producing heat, nearby equipment inducing movement or vibration, nearby equipment emitting vapors, chemicals or particulates, or other potentially interfering or intervening effects.

[0762] The monitoring application 9056 may then analyze the selected subset. In an illustrative example, data from a single component may be analyzed over different time periods such as one operating cycle, several operating cycles, a month, a year, the life of the component or the like. Data from multiple components of the same type may also be analyzed over different time periods. Trends in the data such as changes in frequency or amplitude may be correlated with failure and maintenance records associated with the same or a related component or piece of equipment. Trends in the data, such as changing rates of change associated with start-up or different points in the process, may be identified. Additional data may be introduced into the analysis such as output product quality, output quantity (such as per unit of time), indicated success or failure of a process, and the like. Correlation of trends and values for different types of data may be analyzed to identify those parameters whose short-term analysis might provide the best prediction regarding expected performance. This information may be transmitted back to the monitoring device to update types of data collected and analyzed locally or to influence the design of future monitoring devices.

[0763] In embodiments, the monitoring application 9056 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for a plurality of component types, operational history, historical detection values, component life models and the like for use analyzing the selected subset using rule-based or model-based analysis. In embodiments, the monitoring application 9056 may feed a neural net with the selected subset to learn to recognize peaks in waveform patterns by feeding a large data set sample of waveform behavior of a given type within which peaks are designated (such as by human analysts).

[0764] A monitoring system for data collection in an industrial environment, the monitoring system comprising: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a peak detection circuit structured to determine at least one peak value in response to the plurality of detection values; and a peak response circuit structured to perform at least one operation in response to the at least one peak value.

[0765] An example monitoring system further includes: wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one of the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one of the plurality of detection values' wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible or visual; further comprising a data storage circuit, wherein the relative phase difference and at least one of the detection values and the timing signal are stored wherein the at least one operation further comprises storing additional data in the data storage circuit wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference wherein the data acquisition circuit further comprises at least one multiplexer circuit whereby alternative combinations of detection values may be selected based on at least one of user input and a selected operating parameter for a machine, wherein each of the plurality of detection values corresponds to at least one of the input sensors wherein the at least one operation comprises enabling or disabling one or more portions of the multiplexer circuit, or altering the multiplexer control lines wherein the data acquisition circuit comprises at least two multiplexer circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits.

[0766] A monitoring system for data collection in an industrial environment, the monitoring system structure to receive input corresponding to a plurality of sensors, includes a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input sensors; a peak detection circuit structured to determine at least one peak value in response to the plurality of detection values; and a peak response circuit structured to perform at least one operation in response to the at least one peak value.

[0767] An example monitoring system further includes: wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one of the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one of the plurality of detection values wherein the at least one operation comprises issuing an alert wherein the alert may be one of haptic, audible or visual further comprising a data storage circuit, wherein the relative phase difference and at least one of the detection values and the timing signal are stored wherein the at least one operation further comprises storing additional data in the data storage circuit wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference wherein the data acquisition circuit further comprises at least one multiplexer circuit whereby alternative combinations of detection values may be selected based on at least one of user input and a selected operating parameter for a machine, wherein each of the plurality of detection values corresponds to at least one of the input sensors wherein the at least one operation comprises enabling or disabling one or more portions of the multiplexer circuit, or altering the multiplexer control lines wherein the data acquisition circuit comprises at least two multiplexer circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits.

[0768] An example system for data collection, processing, and utilization of signals in an industrial environment includes: a plurality of monitoring devices, each monitoring device comprising: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a peak detection circuit structured to determine at least one peak value in response to the plurality of detection values; a peak response circuit structured to select at least one detection value in response to the at least one peak value; a communication circuit structured to communicate the at least one selected detection value to a remote server; and a monitoring application on the remote server structured to: receive the at least one selected detection value; jointly analyze received detection values from a subset of the plurality of monitoring devices; and recommend an action.

[0769] An example system further includes: the system further structured to subset detection values based on one of anticipated life of a component associated with detection values, type of the equipment associated with detection values, and operational conditions under which detection values were measured; wherein the analysis of the subset of detection values comprises feeding a neural net with the subset of detection values and supplemental information to learn to recognize various operating states, health states, life expectancies and fault states utilizing deep learning techniques; wherein the supplemental information comprises one of component specification, component performance, equipment specification, equipment performance, maintenance records, repair records and an anticipated state model wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one the plurality of detection values wherein the at least one operation comprises issuing an alert wherein the alert may be one of haptic, audible and visual further comprising a data storage circuit, wherein the relative phase difference and at least one of the detection values and the timing signal are stored wherein the at least one operation further comprises storing additional data in the data storage circuit wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference wherein the data acquisition circuit further comprises at least one multiplexer circuit whereby alternative combinations of detection values may be selected based on at least one of user input and a selected operating parameter for a machine, wherein each of the plurality of detection values corresponds to at least one of the input sensors wherein the at least one operation comprises enabling or disabling one or more portions of the multiplexer circuit, or altering the multiplexer control lines and/or wherein the data acquisition circuit comprises at least two multiplexer circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits.

[0770] An example motor monitoring system, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the motor and motor components, store historical motor performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in a motor performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and a motor system performance parameter.

[0771] An example system for estimating a vehicle steering system performance parameter, the device includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the vehicle steering system, the rack, the pinion, and the steering column, store historical steering system performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in a vehicle steering system performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and a vehicle steering system performance parameter.

[0772] An example system for estimating a pump performance parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the pump and pump components associated with the detection values, store historical pump performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in a pump performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and a pump performance parameter. In certain further embodiments, the example system includes wherein the pump is a water pump in a car and wherein the pump is a mineral pump.

[0773] An example system for estimating a drill performance parameter for a drilling machine, includes a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the drill and drill components associated with the detection values, store historical drill performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in a drill performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and a drill performance parameter. An example system further includes wherein the drilling machine is one of an oil drilling machine and a gas drilling machine.

[0774] An example system for estimating a conveyor health parameter, the system includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a conveyor and conveyor components associated with the detection values, store historical conveyor performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in a conveyor performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and a conveyor performance parameter.

[0775] An example system for estimating an agitator health parameter, the system includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for an agitator and agitator components associated with the detection values, store historical agitator performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in an agitator performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and an agitator performance parameter. In certain embodiments, a system further includes where the agitator is one of a rotating tank mixer, a large tank mixer, a portable tank mixer, a tote tank mixer, a drum mixer, a mounted mixer and a propeller mixer.

[0776] An example system for estimating a compressor health parameter, the system includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a compressor and compressor components associated with the detection values, store historical compressor performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in a compressor performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and a compressor performance parameter.

[0777] An example system for estimating an air conditioner health parameter, the system includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for an air conditioner and air conditioner components associated with the detection values, store historical air conditioner performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value, a pressure value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in an air conditioner performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and an air conditioner performance parameter.

[0778] An example system for estimating a centrifuge health parameter, the system includes: a data acquisition circuit structured to interpret a plurality of detection values from a plurality of input sensors communicatively coupled to the data acquisition circuit, each of the plurality of detection values corresponding to at least one of the input sensors; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a centrifuge and centrifuge components associated with the detection values, store historical centrifuge performance and buffer the plurality of detection values for a predetermined length of time; a peak detection circuit structured to determine a plurality of peak values comprising at least a temperature peak value, a speed peak value and a vibration peak value in response to the plurality of detection values and analyze the peak values relative to buffered detection values, specifications and anticipated state information resulting in a centrifuge performance parameter; and a peak response circuit structured to perform at least one operation in response to one of a peak value and a centrifuge performance parameter.

[0779] Bearings are used throughout many different types of equipment and applications. Bearings may be present in or supporting shafts, motors, rotors, stators, housings, frames, suspension systems and components, gears, gear sets of various types, other bearings, and other elements. Bearings may be used as support for high speed vehicles such as maglev trains. Bearings are used to support rotating shafts for engines, motors, generators, fans, compressors, turbines and the like. Giant roller bearings may be used to support buildings and physical infrastructure. Different types of bearings may be used to support conventional, planetary and other types of gears. Bearings may be used to support transmissions and gearboxes such as roller thrust bearings, for example. Bearings may be used to support wheels, wheel hubs and other rolling parts using tapered roller bearings.

[0780] There are many different types of bearings such as roller bearings, needle bearings, sleeve bearings, ball bearings, radial bearings, thrust load bearings including ball thrust bearings used in low speed applications and roller thrust bearings, taper bearings and tapered roller bearings, specialized bearings, magnetic bearings, giant roller bearings, jewel bearings (e.g., Sapphire), fluid bearings, flexure bearings to support bending element loads, and the like. References to bearings throughout this disclosure is intended to include, but not be limited by, the terms listed above.

[0781] In embodiments, information about the health or other status or state information of or regarding a bearing in a piece of industrial equipment or in an industrial process may be obtained by monitoring the condition of various components of the industrial equipment or industrial process. Monitoring may include monitoring the amplitude and/or frequency and/or phase of a sensor signal measuring attributes such as temperature, humidity, acceleration, displacement and the like.

[0782] An embodiment of a data monitoring device 9200 is shown in FIG. 85 and may include a plurality of sensors 9206 communicatively coupled to a controller 9202. The controller 9202 may include a data acquisition circuit 9204, a data storage circuit 9216, a signal evaluation circuit 9208 and, optionally, a response circuit 9210. The signal evaluation circuit 9208 may comprise a frequency transformation circuit 9212 and a frequency evaluation circuit 9214.

[0783] The plurality of sensors 9206 may be wired to ports 9226 (reference FIG. 86) on the data acquisition circuit 9204. The plurality of sensors 9206 may be wirelessly connected to the data acquisition circuit 9204. The data acquisition circuit 9204 may be able to access detection values corresponding to the output of at least one of the plurality of sensors 9206 where the sensors 9206 may be capturing data on different operational aspects of a bearing or piece of equipment or infrastructure.

[0784] The selection of the plurality of sensors 9206 for a data monitoring device 9200 designed for a specific bearing or piece of equipment may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, reliability of the sensors, and the like. The impact of failure may drive the extent to which a bearing or piece of equipment is monitored with more sensors and/or higher capability sensors being dedicated to systems where unexpected or undetected bearing failure would be costly or have severe consequences.

[0785] The signal evaluation circuit 9208 may process the detection values to obtain information about a bearing being monitored. The frequency transformation circuit 9212 may transform one or more time-based detection values to frequency information. The transformation may be accomplished using techniques such as a digital Fast Fourier transform (“FFT”), Laplace transform, Z-transform, wavelet transform, other frequency domain transform, or other digital or analog signal analysis techniques, including, without limitation, complex analysis, including complex phase evolution analysis.

[0786] The frequency evaluation circuit 9214 (or frequency analysis circuit) may be structured to detect signals at frequencies of interest. Frequencies of interest may include frequencies higher than the frequency at which the equipment rotates (as measured by a tachometer, for instance), various harmonics and/or resonant frequencies associated with the equipment design and operating conditions such as multiples of shaft rotation velocities or other rotating components for the equipment that is borne by the bearings. Changes in energy at frequencies close to the operating frequency may be an indicator of balance/imbalance in the system. Changes in energy at frequencies on the order of twice the operating frequency may be indicative of a system misalignment—for example, on the coupling, or a looseness in the system, (e.g., rattling at harmonics of the operating frequency). Changes in energy at frequencies close to three or four times the operating frequency, corresponding to the number of bolts on a coupling, may indicate wear of on one of the couplings. Changes in energy at frequencies of four, five, or more times the operating frequency may relate back to something that has a corresponding number of elements, such as if there are energy peaks or activity around five times the operating frequency there may be wear or an imbalance in a five-vane pump or the like.

[0787] In an illustrative and non-limiting example, in the analysis of roller bearings, frequencies of interest may include ball spin frequencies, cage spin frequencies, inner race frequency (as bearings often sit on a race inside a cage), outer race frequency and the like. Bearings that are damaged or beginning to fail may show humps of energy at the frequencies mentioned above and elsewhere in this disclosure. The energy at these frequencies may increase over time as the bearings wear more and become more damaged due to more variations in rotational acceleration and pings.

[0788] In an illustrative and non-limiting example, bad bearings may show humps of energy and the intensity of high frequency measurements may start to grow over time as bearings wear and become imperfect (greater acceleration and pings may show up in high frequency measurement domains). Those measurements may be indicators of air gaps in the bearing system. As bearings begin to wear, harder hits may cause the energy signal to move to higher frequencies.

[0789] In embodiments, the signal evaluation circuit 9208 may also include one or more of a phase detection circuit, a phase lock loop circuit, a bandpass filter circuit, a peak detection circuit, and the like.

[0790] In embodiments, the signal evaluation circuit 9208 may include a transitory signal analysis circuit. Transient signals may cause small amplitude vibrations. However, the challenge in bearing analysis is that you may receive a signal associated with a single or non-periodic impact and an exponential decay. Thus, the oscillation of the bearing may not be represented by a single sine wave, but rather by a spectrum of many high frequency sine waves. For example, a signal from a failing bearing may only be seen, in a time-based signal, as a low amplitude spike for a short amount of time. A signal from a failing bearing may be lower in amplitude than a signal associated with an imbalance even though the consequences of a failed bearing may be more significant. It is important to be able to identify these signals. This type of low amplitude, transient signal may be best analyzed using transient analysis rather than a conventional frequency transformation, such as an FFT, which would treat the signal like a low frequency sine wave. A higher resolution data stream may also provide additional data for the detection of transitory signals in low speed operations. The identification of transitory signals may enable the identification of defects in a piece of equipment or component operating at low RPMs.

[0791] In embodiments, the transitory signal analysis circuit for bearing analysis may include envelope modulation analysis and other transitory signal analysis techniques. The signal evaluation circuit 9208 may store long stream of detection values to the data storage circuit 9216. The transitory signal analysis circuit may use envelope analysis techniques on those long streams of detection values to identify transient effects (such as impacts) which may not be identified by conventional sine wave analysis (such as FFTs).

[0792] The signal evaluation circuit 9208 may utilize transitory signal analysis models optimized for the type of component being measured such as bearings, gears, variable speed machinery and the like. In an illustrative and non-limiting example, a gear may resonate close to its average rotational speed. In an illustrative and non-limiting example, a bearing may resonate close to the bearing rotation frequency and produce a ringing in amplitude around that frequency. For example, if the shaft inner race is wearing there may be chatter between the inner race and the shaft resulting in amplitude modulation to the left and right of the bearing frequency. The amplitude modulation may demonstrate its own sine wave characteristics with its own side bands. Various signal processing techniques may be used to eliminate the sinusoidal component, resulting in a modulation envelope for analysis.

[0793] The signal evaluation circuit 9208 may be optimized for variable speed machinery. Historically, variable speed machinery was expensive to make, and it was common to use DC motors and variable sheaves, such that flow could be controlled using vanes. Variable speed motors became more common with solid-state drive advances (“SCR devices”). The base operating frequency of equipment may be varied from the 50-60 Hz provided by standard utility companies and either and slowed down or sped up to run the equipment at different speeds depending on the application. The ability to run the equipment at varying speeds may result in energy savings. However, depending on the equipment geometry, there may be some speeds which create vibrations at resonant frequencies, reducing the life of the components. Variable speed motors may also emit electricity into bearings which may damage the bearings. In embodiments, the analysis of long data streams for envelope modulation analysis and other transitory signal analysis techniques as described herein may be useful in identifying these frequencies such that control schemes for the equipment may be designed to avoid those speeds which result in unacceptable vibrations and/or damage to the bearings.

[0794] In an illustrative and non-limiting example, heating, ventilation and air conditioning (“HVAC”) systems may be assembled on site using variable speed motors, fans, belts, compressors and the like where the operating speeds are not constant, and their relative relationships are unknown. In an illustrative and non-limiting example, variable speed motors may be used in fan pumps for building air circulation. Variable speed motors may be used to vary the speed of conveyors—for example, in manufacturing assembly lines or steel mills. Variable speed motors may be used for fans in a pharmaceutical process, such as where it may be critical to avoid vibration.

[0795] In an illustrative and non-limiting example, sleeve bearings may be analyzed for defects. Sleeve bearings typically have an oil system. If the oil flow stops or the oil becomes severely contaminated, failure can occur very quickly. Therefore, a fluid particulate sensor or fluid pressure sensors may be an important source of detection values.

[0796] In an illustrative and non-limiting example, fan integrity may be evaluated by measuring air pulsations related to blade pass frequencies. For example, if a fan has 12 blades, 12 air pulsations may be measured. Variations in the amplitude of the pulsations associated with the different blades may be indicative of changes in a fan blade. Changes in frequencies associated with the air pulsations may be indicative of bearing problems.

[0797] In an illustrative and non-limiting example, compressors used in the gas and oil field or in gas handling equipment on an assembly line may be evaluated by measuring the periodic increases in energy/pressure in the storage vessel as gas is pumped into the vessel. Periodic variations in the amplitude of the energy increases may be associated with piston wear or damage to a portion of a rotary screw. Phase evaluation of the energy signal relative to timing signals may be helpful in identifying which piston or portion of the rotary screw has damage. Changes in frequencies associated with the energy pulsations may be indicative of bearing problems.

[0798] In an illustrative and non-limiting example, cavitation/air pockets in pumps may create shuttering in the pump housing and the output flow which may be identified with the frequency transformation and frequency analysis techniques described above and elsewhere herein.

[0799] In an illustrative and non-limiting example, the frequency transformation and frequency analysis techniques described above and elsewhere herein may assist in the identification of problems in components of building HVAC systems such as big fans. If the dampers of the system are set poorly it may result in ducts pulsing or vibrating as air is pushed through the system. Monitoring of vibration sensors on the ducts may assist in the balancing of the system. If there are defects in the blades of the big fan this may also result in uneven air flow and resulting pulsation in the buildings ductwork.

[0800] In an illustrative and non-limiting example, detection values from acoustical sensors located close to the bearings may assist in the identification of issues in the engagement between gears or bad bearings. Based on a knowledge of gear ratios, such as the “in” and “out” gear ratios, for a system and measurements of the input and output rotational speed, detection values may be evaluated for energy occurring at those ratios, which in turn may be used to identify bad bearings. This could be done with simple off the shelf motors rather than requiring extensive retrofitting of the motor with sensors.

[0801] Based on the output of its various components, the signal evaluation circuit 9208 may make a bearing life prediction, identify a bearing health parameter, identify a bearing performance parameter, determine a bearing health parameter (e.g., fault conditions), and the like. The signal evaluation circuit 9208 may identify wear on a bearing, identify the presence of foreign matter (e.g., particulates) in the bearings, identify air gaps or a loss of fluid in oil/fluid coated bearings, identify a loss of lubrication in a set of bearings, identify a loss of power for magnetic bearings and the like, identify strain/stress of flexure bearings, and the like. The signal evaluation circuit 9208 may identify optimal operation parameters for a piece of equipment to extend bearing life. The signal evaluation circuit 9208 may identify behavior (resonant wobble) at a selected operational frequency (e.g., shaft rotation rate).

[0802] The signal evaluation circuit 9208 may communicate with the data storage circuit 9216 to access equipment specifications, equipment geometry, bearing specifications, bearing materials, anticipated state information for a plurality of bearing types, operational history, historical detection values, and the like for use in assessing the output of its various components. The signal evaluation circuit 9208 may buffer a subset of the plurality of detection values, intermediate data such as time-based detection values transformed to frequency information, filtered detection values, identified frequencies of interest, and the like for a predetermined length of time. The signal evaluation circuit 9208 may periodically store certain detection values in the data storage circuit 9216 to enable the tracking of component performance over time. In embodiments, based on relevant operating conditions and/or failure modes that may occur as detection values approach one or more criteria, the signal evaluation circuit 9208 may store data in the data storage circuit 9216 based on the fit of data relative to one or more criteria, such as those described throughout this disclosure. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 9208 may store additional data such as RPMs, component loads, temperatures, pressures, vibrations or other sensor data of the types described throughout this disclosure in the data storage circuit 9216. The signal evaluation circuit 9208 may store data at a higher data rate for greater granularity in future processing, the ability to reprocess at different sampling rates, and/or to enable diagnosing or post-processing of system information where operational data of interest is flagged, and the like.

[0803] Depending on the type of equipment, the component being measured, the environment in which the equipment is operating and the like, sensors 9206 may comprise one or more of, without limitation, a vibration sensor, an optical vibration sensor, a thermometer, a hygrometer, a voltage sensor, a current sensor, an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition and/or spectral analysis, and/or object position or movement), an image sensor, a structured light sensor, a laser-based image sensor, an infrared sensor, an acoustic wave sensor, a heat flux sensor, a displacement sensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axial vibration sensor, an accelerometer, a tachometer, a fluid pressure meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid particle detector, an acoustical sensor, a pH sensor, and the like, including, without limitation, any of the sensors described throughout this disclosure and the documents incorporated by reference. The sensors may typically comprise at least a temperature sensor, a load sensor, a tri-axial sensor and a tachometer.

[0804] The sensors 9206 may provide a stream of data over time that has a phase component, such as relating to acceleration or vibration, allowing for the evaluation of phase or frequency analysis of different operational aspects of a piece of equipment or an operating component. The sensors 9206 may provide a stream of data that is not conventionally phase-based, such as temperature, humidity, load, and the like. The sensors 9206 may provide a continuous or near continuous stream of data over time, periodic readings, event-driven readings, and/or readings according to a selected interval or schedule.

[0805] In embodiments, as illustrated in FIG. 85, the sensors 9206 may be part of the data monitoring device 9200, referred to herein in some cases as a data collector, which in some cases may comprise a mobile or portable data collector. In embodiments, as illustrated in FIGS. 86 and 87, one or more external sensors 9224, which are not explicitly part of a monitoring device 9218 but rather are new, previously attached to or integrated into the equipment or component, may be opportunistically connected to or accessed by the monitoring device 9218. The monitoring device 9218 may include a controller 9220. The controller 9202 may include a data acquisition circuit 9222, a data storage circuit 9216, a signal evaluation circuit 9208 and, optionally, a response circuit 9210. The signal evaluation circuit 9208 may comprise a frequency transformation circuit 9212 and a frequency analysis circuit 9214. The data acquisition circuit 9222 may include one or more input ports 9226. The one or more external sensors 9224 may be directly connected to the one or more input ports 9226 on the data acquisition circuit 9222 of the controller 9220 or may be accessed by the data acquisition circuit 9222 wirelessly, such as by a reader, interrogator, or other wireless connection, such as over a short-distance wireless protocol. In embodiments as shown in FIG. 87, a data acquisition circuit 9222 may further comprise a wireless communications circuit 9262. The data acquisition circuit 9222 may use the wireless communications circuit 9262 to access detection values corresponding to the one or more external sensors 9224 wirelessly or via a separate source or some combination of these methods.

[0806] In embodiments, as illustrated in FIG. 88, the data acquisition circuit 9222 may further comprise a multiplexer circuit 9236 as described elsewhere herein. Outputs from the multiplexer circuit 9236 may be utilized by the signal evaluation circuit 9208. The response circuit 9210 may have the ability to turn on and off portions of the multiplexor circuit 9236. The response circuit 9210 may have the ability to control the control channels of the multiplexor circuit 9236.

[0807] The response circuit 9210 may initiate actions based on a bearing performance parameter, a bearing health value, a bearing life prediction parameter, and the like. The response circuit 9210 may evaluate the results of the signal evaluation circuit 9208 and, based on certain criteria or the output from various components of the signal evaluation circuit 9208, initiate an action. The criteria may include a sensor's detection values at certain frequencies or phases relative to a timer signal where the frequencies or phases of interest may be based on the equipment geometry, equipment control schemes, system input, historical data, current operating conditions, and/or an anticipated response. The criteria may include a sensor's detection values at certain frequencies or phases relative to detection values of a second sensor. The criteria may include signal strength at certain resonant frequencies/harmonics relative to detection values associated with a system tachometer or anticipated based on equipment geometry and operation conditions. Criteria may include a predetermined peak value for a detection value from a specific sensor, a cumulative value of a sensor's corresponding detection value over time, a change in peak value, a rate of change in a peak value, and/or an accumulated value (e.g., a time spent above/below a threshold value, a weighted time spent above/below one or more threshold values, and/or an area of the detected value above/below one or more threshold values). The criteria may comprise combinations of data from different sensors such as relative values, relative changes in value, relative rates of change in value, relative values over time, and the like. The relative criteria may change with other data or information such as process stage, type of product being processed, type of equipment, ambient temperature and humidity, external vibrations from other equipment, and the like. The relative criteria may be reflected in one or more calculated statistics or metrics (including ones generated by further calculations on multiple criteria or statistics), which in turn may be used for processing (such as on-board a data collector or by an external system), such as to be provided as an input to one or more of the machine learning capabilities described in this disclosure, to a control system (which may be on board a data collector or remote, such as to control selection of data inputs, multiplexing of sensor data, storage, or the like), or as a data element that is an input to another system, such as a data stream or data package that may be available to a data marketplace, a SCADA system, a remote control system, a maintenance system, an analytic system, or other system.

[0808] Certain embodiments are described herein as detected values exceeding thresholds or predetermined values, but detected values may also fall below thresholds or predetermined values—for example, where an amount of change in the detected value is expected to occur, but detected values indicate that the change may not have occurred. For example, and without limitation, vibrational data may indicate system agitation levels, properly operating equipment, or the like, and vibrational data below amplitude and/or frequency thresholds may be an indication of a process that is not operating according to expectations. Except where the context clearly indicates otherwise, any description herein describing a determination of a value above a threshold and/or exceeding a predetermined or expected value is understood to include determination of a value below a threshold and/or falling below a predetermined or expected value.

[0809] The predetermined acceptable range may be based on anticipated system response or vibration based on the equipment geometry and control scheme such as number of bearings, relative rotational speed, influx of power to the system at a certain frequency, and the like. The predetermined acceptable range may also be based on long term analysis of detection values across a plurality of similar equipment and components and correlation of data with equipment failure.

[0810] In some embodiments, an alert may be issued based on some of the criteria discussed above. In an illustrative example, an increase in temperature and energy at certain frequencies may indicate a hot bearing that is starting to fail. In embodiments, the relative criteria for an alarm may change with other data or information such as process stage, type of product being processed on equipment, ambient temperature and humidity, external vibrations from other equipment and the like. In an illustrative and non-limiting example, the response circuit 9210 may initiate an alert if a vibrational amplitude and/or frequency exceeds a predetermined maximum value, if there is a change or rate of change that exceeds a predetermined acceptable range, and/or if an accumulated value based on vibrational amplitude and/or frequency exceeds a threshold.

[0811] In embodiments, response circuit 9210 may cause the data acquisition circuit 9204 to enable or disable the processing of detection values corresponding to certain sensors based on some of the criteria discussed above. This may include switching to sensors having different response rates, sensitivity, ranges, and the like, or accessing new sensors or types of sensors, and the like. Switching may be undertaken based on a model, a set of rules, or the like. In embodiments, switching may be under control of a machine learning system, such that switching is controlled based on one or more metrics of success, combined with input data, over a set of trials, which may occur under supervision of a human supervisor or under control of an automated system. Switching may involve switching from one input port to another (such as to switch from one sensor to another). Switching may involve altering the multiplexing of data, such as combining different streams under different circumstances. Switching may also involve activating a system to obtain additional data, such as moving a mobile system (such as a robotic or drone system), to a location where different or additional data is available (such as positioning an image sensor for a different view or positioning a sonar sensor for a different direction of collection) or to a location where different sensors can be accessed (such as moving a collector to connect up to a sensor that is disposed at a location in an environment by a wired or wireless connection). This switching may be implemented by changing the control signals for a multiplexor circuit 9236 and/or by turning on or off certain input sections of the multiplexor circuit 9236. The response circuit 9210 may make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 9210 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like.

[0812] In embodiments, the response circuit 9210 may recommend maintenance at an upcoming process stop or initiate a maintenance call. The response circuit 9210 may recommend changes in process or operating parameters to remotely balance the piece of equipment. In embodiments, the response circuit 9210 may implement or recommend process changes—for example to lower the utilization of a component that is near a maintenance interval, operating off-nominally, or failed for purpose but still at least partially operational, to change the operating speed of a component (such as to put it in a lower-demand mode), to initiate amelioration of an issue (such as to signal for additional lubrication of a roller bearing set, or to signal for an alignment process for a system that is out of balance), and the like.

[0813] In embodiments as shown in FIGS. 89, 90, 91, and 92, a data monitoring system 9240 may include at least one data monitoring device 9250. The at least one data monitoring device 9250 may include sensors 9206 and a controller 9242 comprising a data acquisition circuit 9204, a signal evaluation circuit 9208, a data storage circuit 9216, and a communications circuit 9246. The signal evaluation circuit 9208 may include at least one of a frequency detection circuit 9212 and a frequency analysis circuit 9214. There may also be an optional response circuit as described above and elsewhere herein. The signal evaluation circuit 9208 may periodically share data with the communication circuit 9246 for transmittal to a remote server 9244 to enable the tracking of component and equipment performance over time and under varying conditions by a monitoring application 9248. Because relevant operating conditions and/or failure modes may occur as sensor values approach one or more criteria, the signal evaluation circuit 9208 may share data with the communication circuit 9246 for transmittal to the remote server 9244 based on the fit of data relative to one or more criteria. Based on one sensor input meeting or approaching specified criteria or range, the signal evaluation circuit 9208 may share additional data such as RPMs, component loads, temperatures, pressures, vibrations, and the like for transmittal. The signal evaluation circuit 9208 may share data at a higher data rate for transmittal to enable greater granularity in processing on the remote server.

[0814] In embodiments, as shown in FIG. 89, the communications circuit 9246 may communicate data directly to a remote server 9244. In embodiments, as shown in FIG. 90, the communications circuit 9246 may communicate data to an intermediate computer 9252, which may include a processor 9254 running an operating system 9256 and a data storage circuit 9258. The intermediate computer 9252 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 9244.

[0815] In embodiments, as illustrated in FIGS. 91 and 92, a data collection system 9260 may have a plurality of monitoring devices 9250 collecting data on multiple components in a single piece of equipment, collecting data on the same component across a plurality of pieces of equipment, (both the same and different types of equipment) in the same facility as well as collecting data from monitoring devices in multiple facilities. A monitoring application 9248 on a remote server 9244 may receive and store one or more of the following: detection values, timing signals and data coming from a plurality of the various monitoring devices 9250. In embodiments, as shown in FIG. 91, the communications circuit 9246 may communicate data directly to a remote server 9244. In embodiments, as shown in FIG. 92, the communications circuit 9246 may communicate data to an intermediate computer 9252, which may include a processor 9254 running an operating system 9256 and a data storage circuit 9258. There may be an individual intermediate computer 9252 associated with each monitoring device 9250 or an individual intermediate computer 9252 may be associated with a plurality of monitoring devices 9250 where the intermediate computer 9252 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 9244.

[0816] The monitoring application 9248 may select subsets of the detection values, timing signals and data to be jointly analyzed. Subsets for analysis may be selected based on a bearing type, bearing materials, or a single type of equipment in which a bearing is operating. Subsets for analysis may be selected or grouped based on common operating conditions or operational history such as size of load, operational condition (e.g., intermittent, continuous), operating speed or tachometer, common ambient environmental conditions such as humidity, temperature, air or fluid particulate, and the like. Subsets for analysis may be selected based on common anticipated state information. Subsets for analysis may be selected based on the effects of other nearby equipment such as nearby machines rotating at similar frequencies, nearby equipment producing electromagnetic fields, nearby equipment producing heat, nearby equipment inducing movement or vibration, nearby equipment emitting vapors, chemicals or particulates, or other potentially interfering or intervening effects.

[0817] The monitoring application 9248 may analyze a selected subset. In an illustrative example, data from a single component may be analyzed over different time periods, such as one operating cycle, cycle-to-cycle comparisons, trends over several operating cycles/times such as a month, a year, the life of the component, or the like. Data from multiple components of the same type may also be analyzed over different time periods. Trends in the data such as changes in frequency or amplitude may be correlated with failure and maintenance records associated with the same component or piece of equipment. Trends in the data such as changing rates of change associated with start-up or different points in the process may be identified. Additional data may be introduced into the analysis such as output product quality, output quantity (such as per unit of time), indicated success or failure of a process, and the like. Correlation of trends and values for different types of data may be analyzed to identify those parameters whose short-term analysis might provide the best prediction regarding expected performance. The analysis may identify model improvements to the model for anticipated state information, recommendations around sensors to be used, positioning of sensors and the like. The analysis may identify additional data to collect and store. The analysis may identify recommendations regarding needed maintenance and repair and/or the scheduling of preventative maintenance. The analysis may identify recommendations around purchasing replacement bearings and the timing of the replacement of the bearings. The analysis may result in warning regarding the dangers of catastrophic failure conditions. This information may be transmitted back to the monitoring device to update types of data collected and analyzed locally or to influence the design of future monitoring devices.

[0818] In embodiments, the monitoring application 9248 may have access to equipment specifications, equipment geometry, bearing specifications, bearing materials, anticipated state information for a plurality of bearing types, operational history, historical detection values, bearing life models and the like for use analyzing the selected subset using rule-based or model-based analysis. In embodiments, the monitoring application 9248 may feed a neural net with the selected subset to learn to recognize various operating state, health states (e.g., lifetime predictions) and fault states utilizing deep learning techniques. In embodiments, a hybrid of the two techniques (model-based learning and deep learning) may be used.

[0819] In an illustrative and non-limiting example, the health of bearings on conveyors and lifters in an assembly line, in water pumps on industrial vehicles and in compressors in gas handling systems, in compressors situated out in the gas and oil fields, in factory air conditioning units and in factory mineral pumps may be monitored using the frequency transformation and frequency analysis techniques, data monitoring devices and data collection systems described herein.

[0820] In an illustrative and non-limiting example, the health of one or more of bearings, gears, blades, screws and associated shafts, motors, rotors, stators, gears, and other components of gearboxes, motors, pumps, vibrating conveyors, mixers, centrifuges, drilling machines, screw drivers and refining tanks situated in the oil and gas fields may be evaluated using the frequency transformation and frequency analysis techniques, data monitoring devices and data collection systems described herein.

[0821] In an illustrative and non-limiting example, the health of bearings and associated shafts, motors, rotors, stators, gears, and other components of rotating tank/mixer agitators, mechanical/rotating agitators, and propeller agitators, to promote chemical reactions deployed in chemical and pharmaceutical production lines may be evaluated using the frequency transformation and frequency analysis techniques, data monitoring devices and data collection systems described herein.

[0822] In an illustrative and non-limiting example, the health of bearings and associated shafts, motors, rotors, stators, gears, and other components of vehicle systems such as steering mechanisms or engines may be evaluated using the frequency transformation and frequency analysis techniques, data monitoring devices and data collection systems described herein.

[0823] An example monitoring device for bearing analysis in an industrial environment, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time; and a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter.

[0824] In certain further embodiments, an example monitoring device includes one or more of: a response circuit to perform at least one operation in response to the bearing performance parameter, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer; wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one of the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one of the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; wherein the at least one operation further comprises storing additional data in the data storage circuit; wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference.

[0825] An example monitoring device forbearing analysis in an industrial environment, the monitoring device includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time; and a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing health value.

[0826] In certain embodiments, an example monitoring device further includes one or more of: a response circuit to perform at least one operation in response to the bearing health value, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer; wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one of the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one of the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; wherein the at least one operation further comprises storing additional data in the data storage circuit; wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference.

[0827] An example monitoring device for bearing analysis in an industrial environment, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time; and a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing life prediction parameter.

[0828] In certain embodiments, a monitoring device further includes one or more of: a response circuit to perform at least one operation in response to the bearing life prediction parameter, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer; wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one of the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one of the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; wherein the at least one operation further comprises storing additional data in the data storage circuit; wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference.

[0829] An example monitoring device for bearing analysis in an industrial environment, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time; and a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter, wherein the data acquisition circuit comprises a multiplexer circuit whereby alternative combinations of the detection values may be selected based on at least one of user input, a detected state and a selected operating parameter for a machine.

[0830] In certain further embodiments, an example monitoring device further includes one or more of: a response circuit to perform at least one operation in response to the bearing performance parameter, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer; a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one of the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one of the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; wherein the at least one operation further comprises storing additional data in the data storage circuit; wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference; wherein the at least one operation comprises enabling or disabling one or more portions of the multiplexer circuit, or altering the multiplexer control lines; wherein the data acquisition circuit comprises at least two multiplexer circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits.

[0831] An example system for data collection, processing, and bearing analysis in an industrial environment includes: a plurality of monitoring devices, each monitoring device comprising: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time;

• • [0832] a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing life prediction; a communication circuit structured to communicate with a remote server providing the bearing life prediction and a portion of the buffered detection values to the remote server; and
  • [0833] a monitoring application on the remote server structured to receive, store and jointly analyze a subset of the detection values from the plurality of monitoring devices.

[0834] In certain further embodiments, an example monitoring device includes one or more of: a response circuit to perform at least one operation in response to the bearing life prediction, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer; wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one of the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one of the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; wherein the at least one operation further comprises storing additional data in the data storage circuit; wherein the storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference.

[0835] An example system for data collection, processing, and bearing analysis in an industrial environment comprising: a plurality of monitoring devices, each comprising: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing specifications and anticipated state information for a plurality of bearing types and buffering the plurality of detection values for a predetermined length of time;

• • [0836] a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; a communication circuit structured to communicate with a remote server providing the life prediction and a portion of the buffered detection values to the remote server; and a monitoring application on the remote server structured to receive, store and jointly analyze a subset of the detection values from the plurality of monitoring devices.

[0837] In certain further embodiments, an example monitoring device further includes one or more of: a response circuit to perform at least one operation in response to the bearing performance parameter, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer; wherein the at least one operation is further in response to at least one of: a change in amplitude of at least one of the plurality of detection values; a change in frequency or relative phase of at least one of the plurality of detection values; a rate of change in both amplitude and relative phase of at least one the plurality of detection values; and a relative rate of change in amplitude and relative phase of at least one the plurality of detection values; wherein the at least one operation comprises issuing an alert; wherein the alert may be one of haptic, audible and visual; wherein the at least one operation further comprises storing additional data in the data storage circuit; wherein storing additional data in the data storage circuit is further in response to at least one of: a change in the relative phase difference and a relative rate of change in the relative phase difference.

[0838] An example system for data collection, processing, and bearing analysis in an industrial environment includes: a plurality of monitoring devices, each monitoring device comprising: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a streaming circuit for streaming at least a subset of the acquired detection values to a remote learning system; and a remote learning system including a bearing analysis circuit structured to analyze the detection values relative to a machine-based understanding of the state of the at least one bearing.

[0839] In certain further embodiments, an example system further includes one or more of: wherein the machine-based understanding is developed based on a model of the bearing that determines a state of the at least one bearing based at least in part on the relationship of the behavior of the bearing to an operating frequency of a component of the industrial machine; wherein the state of the at least one bearing is at least one of an operating state, a health state, a predicted lifetime state and a fault state; wherein the machine-based understanding is developed based by providing inputs to a deep learning machine, wherein the inputs comprise a plurality of streams of detection values for a plurality of bearings and a plurality of measured state values for the plurality of bearings; wherein the state of the at least one bearing is at least one of an operating state, a health state, a predicted lifetime state and a fault state.

[0840] An example method of analyzing bearings and sets of bearings, includes: receiving a plurality of detection values corresponding to data from a temperature sensor, a vibration sensor positioned near the bearing or set of bearings and a tachometer to measure rotation of a shaft associated with the bearing or set of bearings; comparing the detection values corresponding to the temperature sensor to a predetermined maximum level; filtering the detection values corresponding to the vibration sensor through a high pass filter where the filter is selected to eliminate vibrations associated with detection values associated with the tachometer; identifying rapid changes in at least one of a temperature peak and a vibration peak; identifying frequencies at which spikes in the filtered detection values corresponding to the vibration sensor occur and comparing frequencies and spikes in amplitude relative to an anticipated state information and specification associated with the bearing or set of bearings; and determining a bearing health parameter.

[0841] An example device for monitoring roller bearings in an industrial environment, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage circuit structured to store specifications and anticipated state information for a plurality of types of roller bearings and buffering the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and a response circuit to perform at least one operation in response to the bearing performance prediction, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.

[0842] An example device for monitoring sleeve bearings in an industrial environment, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing sleeve bearing specifications and anticipated state information for types of sleeve bearings and buffering the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and

• • [0843] a response circuit to perform at least one operation in response to the bearing performance parameter, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.

[0844] An example system for monitoring pump bearings in an industrial environment, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing pump specifications, bearing specifications, anticipated state information for pump bearings and buffering the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and

• • [0845] a response circuit to perform at least one operation in response to the bearing performance parameter, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor and a tachometer.

[0846] An example system for collection, processing, and analyzing pump bearings in an industrial environment includes: a plurality of monitoring devices, each comprising: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a data storage for storing pump specifications, bearing specifications, anticipated state information for pump bearings and buffering the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to the pump and bearing specifications and anticipated state information resulting in a bearing performance parameter; a communication circuit structured to communicate with a remote server providing the bearing performance parameter and a portion of the buffered detection values to the remote server; and a monitoring application on the remote server structured to receive, store and jointly analyze a subset of the detection values from the plurality of monitoring devices.

[0847] An example system for estimating a conveyor health parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the conveyor and associated rotating components, store historical conveyor and component performance and buffer the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and

• • [0848] a system analysis circuit structured to utilize the bearing performance and at least one of an anticipated state, historical data and a system geometry to estimate a conveyor health performance.

[0849] An example system for estimating an agitator health parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the agitator and associated components, store historical agitator and component performance and buffer the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and a system analysis circuit structured to utilize the bearing performance and at least one of an anticipated state, historical data and a system geometry to estimate an agitation health parameter. In certain further embodiments, an example device further includes where the agitator is one of a rotating tank mixer, a large tank mixer, a portable tank mixers, a tote tank mixer, a drum mixer, a mounted mixer and a propeller mixer.

[0850] An example system for estimating a vehicle steering system performance parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the vehicle steering system, the rack, the pinion, and the steering column, store historical steering system performance and buffer the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and a system analysis circuit structured to utilize the bearing performance and at least one of an anticipated state, historical data and a system geometry to estimate a vehicle steering system performance parameter.

[0851] An example system for estimating a pump performance parameter, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the pump and pump components, store historical steering system performance and buffer the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; a system analysis circuit structured to utilize the bearing performance and at least one of an anticipated state, historical data and a system geometry to estimate a pump performance parameter. In certain embodiments, and example system further includes wherein the pump is a water pump in a car, and/or wherein the pump is a mineral pump.

[0852] An example system for estimating a performance parameter for a drilling machine, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the drilling machine and drilling machine components, store historical drilling machine performance and buffer the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and

• • [0853] a system analysis circuit structured to utilize the bearing performance and at least one of an anticipated state, historical data and a system geometry to estimate a performance parameter for the drilling machine. In certain further embodiments, the drilling machine is one of an oil drilling machine and a gas drilling machine.

[0854] An example system for estimating a performance parameter for a drilling machine, includes: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for the drilling machine and drilling machine components, store historical drilling machine performance and buffer the plurality of detection values for a predetermined length of time; a bearing analysis circuit structured to analyze buffered detection values relative to specifications and anticipated state information resulting in a bearing performance parameter; and a system analysis circuit structured to utilize bearing performance and at least one of an anticipated state, historical data and a system geometry to estimate a performance parameter for the drilling machine.

[0855] Rotating components are used throughout many different types of equipment and applications. Rotating components may include shafts, motors, rotors, stators, bearings, fins, vanes, wings, blades, fans, bearings, wheels, hubs, spokes, balls, rollers, pins, gears and the like. In embodiments, information about the health or other status or state information of or regarding a rotating component in a piece of industrial equipment or in an industrial process may be obtained by monitoring the condition of the component or various other components of the industrial equipment or industrial process and identifying torsion on the component. Monitoring may include monitoring the amplitude and phase of a sensor signal, such as one measuring attributes such as angular position, angular velocity, angular acceleration, and the like.

[0856] An embodiment of a data monitoring device 9248 is shown in FIG. 93 and may include a plurality of sensors 9406 communicatively coupled to a controller 9402. The controller 9402 may include a data acquisition circuit 9404, a data storage circuit 9414, a system evaluation circuit 9408 and, optionally, a response circuit 9410. The system evaluation circuit 9408 may comprise a torsion analysis circuit 9412.

[0857] The plurality of sensors 9406 may be wired to ports on the data acquisition circuit 9404. The plurality of sensors 9406 may be wirelessly connected to the data acquisition circuit 9404. The data acquisition circuit 9404 may be able to access detection values corresponding to the output of at least one of the plurality of sensors 9406 where the sensors 9406 may be capturing data on different operational aspects of a bearing or piece of equipment or infrastructure.

[0858] The selection of the plurality of sensors 9406 for a data monitoring device 9248 designed to assess torsion on a component, such as a shaft, motor, rotor, stator, bearing or gear, or other component described herein, or a combination of components, such as within or comprising a drivetrain or piece of equipment or system, may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, reliability of the sensors, and the like. The impact of failure may drive the extent to which a bearing or piece of equipment is monitored with more sensors and/or higher capability sensors being dedicated to systems where unexpected or undetected bearing failure would be costly or have severe consequences. To assess torsion the sensors may include, among other options, an angular position sensor and/or an angular velocity sensor and/or an angular acceleration sensor.

[0859] The system evaluation circuit 9408 may process the detection values to obtain information about one or more rotating components being monitored. The torsional analysis circuit 9412 may be structured to identify torsion in a component or system, such as based on anticipated state, historical state, system geometry and the like, such as that which is available from the data storage circuit 9414. The torsional analysis circuit 9412 may be structured to identify torsion using a variety of techniques such as amplitude, phase and frequency differences in the detection values from two linear accelerometers positioned at different locations on a shaft. The torsional analysis circuit 9412 may identify torsion using the difference in amplitude and phase between an angular accelerometer on a shaft and an angular accelerometer on a slip ring on the end of the shaft. The torsional analysis circuit 9412 may identify shear stress/elongation on a component using two strain gauges in a half bridge configuration or four strain gauges in a full bridge configuration. The torsional analysis circuit 9412 may use coder based techniques such as markers to identify the rotation of a shaft, bearing, rotor, stator, gear or other rotating component. The markers being assessed may include visual markers such as gear teeth or stripes on a shaft captured by an image sensor, light detector or the like. The markers being assessed may include magnetic components located on the rotating component and sensed by an electromagnetic pickup. The sensor may be a Hall Effect sensor.

[0860] Additional input sensors may include a thermometer, a heat flux sensor, a magnetometer, an axial load sensor, a radial load sensor, an accelerometer, a shear-stress torque sensor, a twist angle sensor and the like. Twist angle may include rotational information at two positions on shaft or an angular velocity or angular acceleration at two positions on a shaft. In embodiments, the sensors may be positioned at different ends of the shaft.

[0861] The torsional analysis circuit 9412 may include one or more of a transient signal analysis circuit and/or a frequency transformation circuit and/or a frequency analysis circuit as described elsewhere herein.

[0862] In embodiments, the transitory signal analysis circuit for torsional analysis may include envelope modulation analysis, and other transitory signal analysis techniques. The system evaluation circuit 9408 may store long stream of detection values to the data storage circuit 9414. The transitory signal analysis circuit may use envelope analysis techniques on those long streams of detection values to identify transient effects (such as impacts) which may not be identified by conventional sine wave analysis (such as FFTs).

[0863] In embodiments, the frequencies of interest may include identifying energy at relation-order bandwidths for rotating equipment. The maximum order observed may comprise a function of the bandwidth of the system and the rotational speed of the component. For varying speeds (run-ups, run-downs, etc.), the minimum RPM may determine the maximum-observed order. In embodiments, there may be torsional resonance at harmonics of the forcing frequency/frequency at which a component is being driven.

[0864] In an illustrative and non-limiting example, the monitoring device may be used to collect and process sensor data to measure torsion on a component. The monitoring device may be in communication with or include a high resolution, high speed vibration sensor to collect data over an extended period of time, enough to measure multiple cycles of rotation. For gear driven equipment, the sampling resolution should be such that the number of samples taken per cycle is at least equal to the number of gear teeth driving the component. It will be understood that a lower sampling resolution may also be utilized, which may result in a lower confidence determination and/or taking data over a longer period of time to develop sufficient statistical confidence. This data may then be used in the generation of a phase reference (relative probe) or tachometer signal for a piece of equipment. This phase reference may be used to align phase data such as velocity and/or positional and/or acceleration data from multiple sensors located at different positions on a component or on different components within a system. This information may facilitate the determination of torsion for different components or the generation of an Operational Deflection Shape (“ODS”), indicating the extent of torsion on one or more components during an operational mode.

[0865] The higher resolution data stream may provide additional data for the detection of transitory signals in low speed operations. The identification of transitory signals may enable the identification of defects in a piece of equipment or component.

[0866] In an illustrative and non-limiting example, the monitoring device may be used to identify mechanical jitter for use in failure prediction models. The monitoring device may begin acquiring data when the piece of equipment starts up through ramping up to operating speed or during operation. Once at operating speed, it is anticipated that the torsional jitter should be minimal and changes in torsion during this phase may be indicative of cracks, bearing faults and the like. Additionally, known torsions may be removed from the signal to facilitate the identification of unanticipated torsions resulting from system design flaws or component wear. Having phase information associated with the data collected at operating speed may facilitate identification of a location of vibration and potential component wear. Relative phase information for a plurality of sensors located throughout a machine may facilitate the evaluation of torsion as it is propagated through a piece of equipment.

[0867] Based on the output of its various components, the system evaluation circuit 9408 may make a component life prediction, identify a component health parameter, identify a component performance parameter, and the like. The system evaluation circuit 9408 may identify unexpected torsion on a rotating component, identify strain/stress of flexure bearings, and the like. The system evaluation circuit 9408 may identify optimal operation parameters for a piece of equipment to reduce torsion and extend component life. The system evaluation circuit 9408 may identify torsion at selected operational frequencies (e.g., shaft rotation rates). Information about operational frequencies causing torsion may facilitate equipment operational balance in the future.

[0868] The system evaluation circuit 9408 may communicate with the data storage circuit 9414 to access equipment specifications, equipment geometry, bearing specifications, component materials, anticipated state information for a plurality of component types, operational history, historical detection values, and the like for use in assessing the output of its various components. The system evaluation circuit 9408 may buffer a subset of the plurality of detection values, intermediate data such as time-based detection values, time-based detection values transformed to frequency information, filtered detection values, identified frequencies of interest, and the like for a predetermined length of time. The system evaluation circuit 9408 may periodically store certain detection values in the data storage circuit 9414 to enable the tracking of component performance over time. In embodiments, based on relevant operating conditions and/or failure modes, which may occur as detection values approach one or more criteria, the system evaluation circuit 9408 may store data in the data storage circuit 9414 based on the fit of data relative to one or more criteria, such as those described throughout this disclosure. Based on one sensor input meeting or approaching specified criteria or range, the system evaluation circuit 9408 may store additional data such as RPM information, component loads, temperatures, pressures, vibrations or other sensor data of the types described throughout this disclosure in the data storage circuit 9414. The system evaluation circuit 9408 may store data in the data storage circuit at a higher data rate for greater granularity in future processing, the ability to reprocess at different sampling rates, and/or to enable diagnosing or post-processing of system information where operational data of interest is flagged, and the like.

[0869] Depending on the type of equipment, the component being measured, the environment in which the equipment is operating and the like, sensors 9406 may comprise, without limitation, one or more of the following: a displacement sensor, an angular velocity sensor, an angular accelerometer, a vibration sensor, an optical vibration sensor, a thermometer, a hygrometer, a voltage sensor, a current sensor, an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition and/or spectral analysis, and/or object position or movement), an image sensor, a structured light sensor, a laser-based image sensor, an infrared sensor, an acoustic wave sensor, a heat flux sensor, a displacement sensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axial vibration sensor, an accelerometer, a tachometer, a fluid pressure meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid particle detector, an acoustical sensor, a pH sensor, and the like, including, without limitation, any of the sensors described throughout this disclosure and the documents incorporated by reference.

[0870] The sensors 9406 may provide a stream of data over time that has a phase component, such as relating to angular velocity, angular acceleration or vibration, allowing for the evaluation of phase or frequency analysis of different operational aspects of a piece of equipment or an operating component. The sensors 9406 may provide a stream of data that is not conventionally phase-based, such as temperature, humidity, load, and the like. The sensors 9406 may provide a continuous or near continuous stream of data over time, periodic readings, event-driven readings, and/or readings according to a selected interval or schedule.

[0871] In an illustrative and non-limiting example, when assessing engine components it may be desirable to remove vibrations due to the timing of piston vibrations or anticipated vibrational input due to crankshaft geometry to assist in identifying other torsional forces on a component. This may assist in assessing the health of such diverse components as a water pump in a vehicle or positive displacement pumps.

[0872] In an illustrative and non-limiting example, torsional analysis and the identification of variations in torsion may assist in the identification of stick-slip in a gear or transfer system. In some cases, this may only occur once per cycle, and phase information may be as important as or more important than the amplitude of the signal in determining system state or behavior.

[0873] In an illustrative and non-limiting example, torsional analysis may assist in the identification, prediction (e.g., timing) and evaluation of lash in a drivetrain and the follow-on torsion resulting from a change in direction or start up, which in turn may be used for controlling a system, assessing needs for maintenance, assessing needs for balancing or otherwise re-setting components, or the like.

[0874] In an illustrative and non-limiting example, when assessing compressors, it may be desirable to remove vibrations due to the timing of piston vibrations or anticipated vibrational input associated with the techniques and geometry used for positive displacement compressors to assist in identifying other torsional forces on a component. This may assist in assessing the health of compressors in such diverse environments as air conditioning units in factories, compressors in gas handling systems in an industrial environment, compressors in oil fields, and other environments as described elsewhere herein.

[0875] In an illustrative and non-limiting example, torsional analysis may facilitate the understanding of the health and expected life of various components associated with the drivetrains of vehicles, such as cranes, bulldozers, tractors, haulers, backhoes, forklifts, agricultural equipment, mining equipment, boring and drilling machines, digging machines, lifting machines, mixers (e.g., cement mixers), tank trucks, refrigeration trucks, security vehicles (e.g., including safes and similar facilities for preserving valuables), underwater vehicles, watercraft, aircraft, automobiles, trucks, trains and the like, as well as drivetrains of moving apparatus, such as assembly lines, lifts, cranes, conveyors, hauling systems, and others. The evaluation of the sensor data with the model of the system geometry and operating conditions may be useful in identifying unexpected torsion and the transmission of that torsion from the motor and drive shaft, from the drive shaft to the universal joint and from the universal joint to one or more wheel axles.

[0876] In an illustrative and non-limiting example, torsional analysis may facilitate in the understanding of the health and expected life of various components associated with train/tram wheels and wheel sets. As discussed above, torsional analysis may facilitate in the identification of stick-slip between the wheels or wheel sets and the rail. The torsional analysis in view of the system geometry may facilitate the identification of torsional vibration due to stick-slip as opposed to the torsional vibration due to the driving geometry connecting the engine to the drive shaft to the wheel axle.

[0877] In embodiments, as illustrated in FIG. 93, the sensors 9406 may be part of the data monitoring device 9248, referred to herein in some cases as a data collector, which in some cases may comprise a mobile or portable data collector. In embodiments, as illustrated in FIGS. 94 and 95, one or more external sensors 9422, which are not explicitly part of a monitoring device 9416 but rather are new, previously attached to or integrated into the equipment or component, may be opportunistically connected to or accessed by the monitoring device 9416. The monitoring device 9416 may include a controller 9418. The controller 9418 may include a data acquisition circuit 9420, a data storage circuit 9414, a system evaluation circuit 9408 and, optionally, a response circuit 9410. The system evaluation circuit 9408 may comprise a torsional analysis circuit 9412. The data acquisition circuit 9420 may include one or more input ports 9424. In embodiments as shown in FIG. 95, a data acquisition circuit 9420 may further comprise a wireless communications circuit 9426. The one or more external sensors 9422 may be directly connected to the one or more input ports 9424 on the data acquisition circuit 9420 of the controller 9418 or may be accessed by the data acquisition circuit 9420 wirelessly using the wireless communications circuit 9426, such as by a reader, interrogator, or other wireless connection, such as over a short-distance wireless protocol. The data acquisition circuit 9420 may use the wireless communications circuit 9426 to access detection values corresponding to the one or more external sensors 9422 wirelessly or via a separate source or some combination of these methods.

[0878] In embodiments, as illustrated in FIG. 96, the data acquisition circuit 9432 may further comprise a multiplexer circuit 9434 as described elsewhere herein. Outputs from the multiplexer circuit 9434 may be utilized by the system evaluation circuit 9408. The response circuit 9410 may have the ability to turn on or off portions of the multiplexor circuit 9434. The response circuit 9410 may have the ability to control the control channels of the multiplexor circuit 9434.

[0879] The response circuit 9410 may initiate actions based on a component performance parameter, a component health value, a component life prediction parameter, and the like. The response circuit 9410 may evaluate the results of the system evaluation circuit 9408 and, based on certain criteria or the output from various components of the system evaluation circuit 9408, may initiate an action. The criteria may include identification of torsion on a component by the torsional analysis circuit. The criteria may include a sensor's detection values at certain frequencies or phases relative to a timer signal where the frequencies or phases of interest may be based on the equipment geometry, equipment control schemes, system input, historical data, current operating conditions, and/or an anticipated response. The criteria may include a sensor's detection values at certain frequencies or phases relative to detection values of a second sensor. The criteria may include signal strength at certain resonant frequencies/harmonics relative to detection values associated with a system tachometer or anticipated based on equipment geometry and operation conditions. Criteria may include a predetermined peak value for a detection value from a specific sensor, a cumulative value of a sensor's corresponding detection value over time, a change in peak value, a rate of change in a peak value, and/or an accumulated value (e.g., a time spent above/below a threshold value, a weighted time spent above/below one or more threshold values, and/or an area of the detected value above/below one or more threshold values). The criteria may comprise combinations of data from different sensors such as relative values, relative changes in value, relative rates of change in value, relative values over time, and the like. The relative criteria may change with other data or information such as process stage, type of product being processed, type of equipment, ambient temperature and humidity, external vibrations from other equipment, and the like. The relative criteria may be reflected in one or more calculated statistics or metrics (including ones generated by further calculations on multiple criteria or statistics), which in turn may be used for processing (such as on board a data collector or by an external system), such as to be provided as an input to one or more of the machine learning capabilities described in this disclosure, to a control system (which may be on board a data collector or remote, such as to control selection of data inputs, multiplexing of sensor data, storage, or the like), or as a data element that is an input to another system, such as a data stream or data package that may be available to a data marketplace, a SCADA system, a remote control system, a maintenance system, an analytic system, or other system.

[0880] Certain embodiments are described herein as detected values exceeding thresholds or predetermined values, but detected values may also fall below thresholds or predetermined values—for example where an amount of change in the detected value is expected to occur, but detected values indicate that the change may not have occurred. Except where the context clearly indicates otherwise, any description herein describing a determination of a value above a threshold and/or exceeding a predetermined or expected value is understood to include determination of a value below a threshold and/or falling below a predetermined or expected value.

[0881] The predetermined acceptable range may be based on anticipated torsion based on equipment geometry, the geometry of a transfer system, an equipment configuration or control scheme, such as a piston firing sequence, and the like. The predetermined acceptable range may also be based on historical performance or predicted performance, such as long term analysis of signals and performance both from the past run and from the past several runs. The predetermined acceptable range may also be based on historical performance or predicted performance, or based on long term analysis of signals and performance across a plurality of similar equipment and components (both within a specific environment, within an individual company, within multiple companies in the same industry and across industries). The predetermined acceptable range may also be based on a correlation of sensor data with actual equipment and component performance.

[0882] In some embodiments, an alert may be issued based on some of the criteria discussed above. In embodiments, the relative criteria for an alarm may change with other data or information, such as process stage, type of product being processed on equipment, ambient temperature and humidity, external vibrations from other equipment and the like. In an illustrative and non-limiting example, the response circuit 9410 may initiate an alert if a torsion in a component across a plurality of components exceeds a predetermined maximum value, if there is a change or rate of change that exceeds a predetermined acceptable range, and/or if an accumulated value based on torsion amplitude and/or frequency exceeds a threshold.

[0883] In embodiments, response circuit 9410 may cause the data acquisition circuit 9432 to enable or disable the processing of detection values corresponding to certain sensors based on some of the criteria discussed above. This may include switching to sensors having different response rates, sensitivity, ranges, and the like; accessing new sensors or types of sensors, and the like. Switching may be undertaken based on a model, a set of rules, or the like. In embodiments, switching may be under control of a machine learning system, such that switching is controlled based on one or more metrics of success, combined with input data, over a set of trials, which may occur under supervision of a human supervisor or under control of an automated system. Switching may involve switching from one input port to another (such as to switch from one sensor to another). Switching may involve altering the multiplexing of data, such as combining different streams under different circumstances. Switching may involve activating a system to obtain additional data, such as moving a mobile system (such as a robotic or drone system), to a location where different or additional data is available (such as positioning an image sensor for a different view or positioning a sonar sensor for a different direction of collection) or to a location where different sensors can be accessed (such as moving a collector to connect up to a sensor that is disposed at a location in an environment by a wired or wireless connection). This switching may be implemented by changing the control signals for a multiplexor circuit 9434 and/or by turning on or off certain input sections of the multiplexor circuit 9434.

[0884] The response circuit 9410 may calculate transmission effectiveness based on differences between a measured and theoretical angular position and velocity of an output shaft after accounting for the gear ration and any phase differential between input and output.

[0885] The response circuit 9410 may identify equipment or components that are due for maintenance. The response circuit 9410 may make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 9410 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like.

[0886] In embodiments, the response circuit 9410 may recommend maintenance at an upcoming process stop or initiate a maintenance call. The response circuit 9410 may recommend changes in process or operating parameters to remotely balance the piece of equipment. In embodiments, the response circuit 9410 may implement or recommend process changes—for example to lower the utilization of a component that is near a maintenance interval, operating off-nominally, or failed for purpose but still at least partially operational, to change the operating speed of a component (such as to put it in a lower-demand mode), to initiate amelioration of an issue (such as to signal for additional lubrication of a roller bearing set, or to signal for an alignment process for a system that is out of balance), and the like.

[0887] In embodiments as shown in FIGS. 97, 98, 99, and 100, a data monitoring system 9460 may include at least one data monitoring device 9448. At least one data monitoring device 9448 may include sensors 9406 and a controller 9438 comprising a data acquisition circuit 9404, a system evaluation circuit 9408, a data storage circuit 9414, and a communications circuit 9442. The system evaluation circuit 9408 may include a torsional analysis circuit 9412. There may also be an optional response circuit as described above and elsewhere herein. The system evaluation circuit 9408 may periodically share data with the communication circuit 9442 for transmittal to the remote server 9440 to enable the tracking of component and equipment performance over time and under varying conditions by a monitoring application 9446. Because relevant operating conditions and/or failure modes may occur as sensor values approach one or more criteria, the system evaluation circuit 9408 may share data with the communication circuit 9442 for transmittal to the remote server 9440 based on the fit of data relative to one or more criteria. Based on one sensor input meeting or approaching specified criteria or range, the system evaluation circuit 9408 may share additional data such as RPMs, component loads, temperatures, pressures, vibrations, and the like for transmittal. The system evaluation circuit 9408 may share data at a higher data rate for transmittal to enable greater granularity in processing on the remote server. In embodiments, as shown in FIG. 97, the communications circuit 9442 may communicate data directly to a remote server 9440. In embodiments, as shown in FIG. 98, the communications circuit 9442 may communicate data to an intermediate computer 9450 which may include a processor 9452 running an operating system 9454 and a data storage circuit 9456.

[0888] In embodiments, as illustrated in FIGS. 99 and 100, a data collection system 9460 may have a plurality of monitoring devices 9448 collecting data on multiple components in a single piece of equipment, collecting data on the same component across a plurality of pieces of equipment (both the same and different types of equipment) in the same facility as well as collecting data from monitoring devices in multiple facilities. A monitoring application 9446 on a remote server 9440 may receive and store one or more of detection values, timing signals and data coming from a plurality of the various monitoring devices 9448. In embodiments, as shown in FIG. 99, the communications circuit 9442 may communicate data directly to a remote server 9440. In embodiments, as shown in FIG. 100, the communications circuit 9442 may communicate data to an intermediate computer 9450, which may include a processor 9452 running an operating system 9454 and a data storage circuit 9456. There may be an individual intermediate computer 9450 associated with each monitoring device 9448 or an individual intermediate computer 9450 may be associated with a plurality of monitoring devices 9448 where the intermediate computer 9450 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 9440.

[0889] The monitoring application 9446 may select subsets of detection values, timing signals, data, product performance and the like to be jointly analyzed. Subsets for analysis may be selected based on component type, component materials, or a single type of equipment in which a component is operating. Subsets for analysis may be selected or grouped based on common operating conditions or operational history such as size of load, operational condition (e.g., intermittent, continuous), operating speed or tachometer, common ambient environmental conditions such as humidity, temperature, air or fluid particulate, and the like. Subsets for analysis may be selected based on common anticipated state information. Subsets for analysis may be selected based on the effects of other nearby equipment such as nearby machines rotating at similar frequencies, nearby equipment producing electromagnetic fields, nearby equipment producing heat, nearby equipment inducing movement or vibration, nearby equipment emitting vapors, chemicals or particulates, or other potentially interfering or intervening effects.

[0890] The monitoring application 9446 may analyze a selected subset. In an illustrative example, data from a single component may be analyzed over different time periods such as one operating cycle, cycle to cycle comparisons, trends over several operating cycles/time such as a month, a year, the life of the component or the like. Data from multiple components of the same type may also be analyzed over different time periods. Trends in the data such as changes in frequency or amplitude may be correlated with failure and maintenance records associated with the same component or piece of equipment. Trends in the data such as changing rates of change associated with start-up or different points in the process may be identified. Additional data may be introduced into the analysis such as output product quality, output quantity (such as per unit of time), indicated success or failure of a process, and the like. Correlation of trends and values for different types of data may be analyzed to identify those parameters whose short-term analysis might provide the best prediction regarding expected performance. The analysis may identify model improvements to the model for anticipated state information, recommendations around sensors to be used, positioning of sensors and the like. The analysis may identify additional data to collect and store. The analysis may identify recommendations regarding needed maintenance and repair and/or the scheduling of preventative maintenance. The analysis may identify recommendations around purchasing replacement components and the timing of the replacement of the components. The analysis may identify recommendations regarding future geometry changes to reduce torsion on components. The analysis may result in warning regarding dangers of catastrophic failure conditions. This information may be transmitted back to the monitoring device to update types of data collected and analyzed locally or to influence the design of future monitoring devices.

[0891] In embodiments, the monitoring application 9446 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for a plurality of component types, operational history, historical detection values, component life models and the like for use analyzing the selected subset using rule-based or model-based analysis. In embodiments, the monitoring application 9446 may feed a neural net with the selected subset to learn to recognize various operating states, health states (e.g., lifetime predictions) and fault states utilizing deep learning techniques. In embodiments, a hybrid of the two techniques (model-based learning and deep learning) may be used.

[0892] In an illustrative and non-limiting example, the health of the rotating components on conveyors and lifters in an assembly line may be monitored using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0893] In an illustrative and non-limiting example, the health the rotating components in water pumps on industrial vehicles may be monitored using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0894] In an illustrative and non-limiting example, the health of rotating components in compressors in gas handling systems may be monitored using the data monitoring devices and data collection systems described herein.

[0895] In an illustrative and non-limiting example, the health of the rotating components in compressors situated in the gas and oil fields may be monitored using the data monitoring devices and data collection systems described herein.

[0896] In an illustrative and non-limiting example, the health of the rotating components in factory air conditioning units may be evaluated using the techniques, data monitoring devices and data collection systems described herein.

[0897] In an illustrative and non-limiting example, the health of the rotating components in factory mineral pumps may be evaluated using the techniques, data monitoring devices and data collection systems described herein.

[0898] In an illustrative and non-limiting example, the health of the rotating components such as shafts, bearings, and gears in drilling machines and screw drivers situated in the oil and gas fields may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0899] In an illustrative and non-limiting example, the health of rotating components such as shafts, bearings, gears, and rotors of motors situated in the oil and gas fields may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0900] In an illustrative and non-limiting example, the health of rotating components such as blades, screws and other components of pumps situated in the oil and gas fields may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0901] In an illustrative and non-limiting example, the health of rotating components such as shafts, bearings, motors, rotors, stators, gears, and other components of vibrating conveyors situated in the oil and gas fields may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0902] In an illustrative and non-limiting example, the health of rotating components such as bearings, shafts, motors, rotors, stators, gears, and other components of mixers situated in the oil and gas fields may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0903] In an illustrative and non-limiting example, the health of rotating components such as bearings, shafts, motors, rotors, stators, gears, and other components of centrifuges situated in oil and gas refineries may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0904] In an illustrative and non-limiting example, the health of rotating components such as bearings, shafts, motors, rotors, stators, gears, and other components of refining tanks situated in oil and gas refineries may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0905] In an illustrative and non-limiting example, the health of rotating components such as bearings, shafts, motors, rotors, stators, gears, and other components of rotating tank/mixer agitators to promote chemical reactions deployed in chemical and pharmaceutical production lines may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0906] In an illustrative and non-limiting example, the health of rotating components such as bearings, shafts, motors, rotors, stators, gears, and other components of mechanical/rotating agitators to promote chemical reactions deployed in chemical and pharmaceutical production lines may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0907] In an illustrative and non-limiting example, the health of rotating components such as bearings, shafts, motors, rotors, stators, gears, and other components of propeller agitators to promote chemical reactions deployed in chemical and pharmaceutical production lines may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0908] In an illustrative and non-limiting example, the health of bearings and associated shafts, motors, rotors, stators, gears, and other components of vehicle steering mechanisms may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0909] In an illustrative and non-limiting example, the health of bearings and associated shafts, motors, rotors, stators, gears, and other components of vehicle engines may be evaluated using the torsional analysis techniques, data monitoring devices and data collection systems described herein.

[0910] In embodiments, a monitoring device for estimating an anticipated lifetime of a rotating component in an industrial machine may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in the identification of torsional vibration; and a system analysis circuit structured to utilize the identified torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify an anticipated lifetime of the rotating component. In embodiments, the monitoring device may further comprise a response circuit to perform at least one operation in response to the anticipated lifetime of the rotating component, wherein the plurality of input sensors includes at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor, a tachometer, and the like. At least one operation may comprise issuing at least one of an alert and a warning, storing additional data in the data storage circuit, ordering a replacement of the rotating component, scheduling replacement of the rotating component, recommending alternatives to the rotating component, and the like.

[0911] In embodiments, a monitoring device for evaluating the health of a rotating component in an industrial machine may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in the identification of torsional vibration; and a system analysis circuit structured to utilize the identified torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify the health of the rotating component. In embodiments, the monitoring device may further comprise a response circuit to perform at least one operation in response to the health of the rotating component. The plurality of input sensors may include at least two sensors selected from the group consisting of a temperature sensor, a load sensor, an optical vibration sensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor, an accelerometer, a tri-axial vibration sensor a tachometer, and the like. The monitoring device may issue an alert and an alarm, such as the at least one operation storing additional data in the data storage circuit, ordering a replacement of the rotating component, scheduling replacement of the rotating component, recommending alternatives to the rotating component, and the like.

[0912] In embodiments, a monitoring device for evaluating the operational state of a rotating component in an industrial machine may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in the identification of torsional vibration; and a system analysis circuit structured to utilize the identified torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify the operational state of the rotating component. In embodiments, the operational state may be a current or future operational state. A response circuit may perform at least one operation in response to the operational state of the rotating component. The at least one operation may store additional data in the data storage circuit, order a replacement of the rotating component, schedule a replacement of the rotating component, recommending alternatives to the rotating component, and the like.

[0913] In embodiments, s monitoring device for evaluating the operational state of a rotating component in an industrial machine may include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in the identification of torsional vibration; and a system analysis circuit structured to utilize the identified torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify the operational state of the rotating component, wherein the data acquisition circuit comprises a multiplexer circuit whereby alternative combinations of the detection values may be selected based on at least one of user input, a detected state and a selected operating parameter for a machine. The operational state may be a current or future operational state. The at least one operation may enable or disable one or more portions of the multiplexer circuit, or altering the multiplexer control lines. The data acquisition circuit may include at least two multiplexer circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits.

[0914] In embodiments, a system for evaluating an operational state a rotating component in a piece of equipment may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in identification of any torsional vibration; a system analysis circuit structured to utilize the torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify the operational state of the rotating component; and a communication module enabled to communicate the operational state of the rotating component, the torsional vibration and detection values to a remote server, wherein the detection values communicated are based partly on the operational state of the rotating component and the torsional vibration; and a monitoring application on the remote server structured to receive, store and jointly analyze a subset of the detection values from the monitoring devices. The analysis of the subset of detection values may include transitory signal analysis to identify the presence of high frequency torsional vibration. The monitoring application may be structured to subset detection values based on one of: operational state, torsional vibration, type of the rotating component, operational conditions under which detection values were measured, and type or equipment. The analysis of the subset of detection values may include feeding a neural net with the subset of detection values and supplemental information to learn to recognize various operating states, health states and fault states utilizing deep learning techniques. The supplemental information may include one of component specification, component performance, equipment specification, equipment performance, maintenance records, repair records an anticipated state model, and the like. The operational state may include a current or future operational state. The monitoring device may include a response circuit to perform at least one operation in response to the operational state of the rotating component. The at least one operation may include storing additional data in the data storage circuit.

[0915] In embodiments, a system for evaluating the health of a rotating component in a piece of equipment may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of: an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in identification of torsional vibration; a system analysis circuit structured to utilize the torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify the health of the rotating component; and a communication module enabled to communicate the health of the rotating component, the torsional vibrations and detection values to a remote server, wherein the detection values communicated are based partly on the health of the rotating component and the torsional vibration; and a monitoring application on the remote server structured to receive, store and jointly analyze a subset of the detection values from the monitoring devices. In embodiments, the analysis of the subset of detection values may include transitory signal analysis to identify the presence of high frequency torsional vibration. The monitoring application may be structured to subset detection values. The analysis of the subset of detection values may include feeding a neural net with the subset of detection values and supplemental information to learn to recognize various operating states, health states and fault states utilizing deep learning techniques. The supplemental information may include one of component specification, component performance, equipment specification, equipment performance, maintenance records, repair records and an anticipated state model. The operational state may be a current or future operational state. A response circuit may perform at least one operation in response to the health of the rotating component.

[0916] In embodiments, a system for estimating an anticipated lifetime of a rotating component in a piece of equipment may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in identification of torsional vibration; a system analysis circuit structured to utilize the torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify an anticipated life the rotating component; and a communication module enabled to communicate the anticipated life of the rotating component, the torsional vibrations and detection values to a remote server, wherein the detection values communicated are based partly on the anticipated life of the rotating component and the torsional vibration; and a monitoring application on the remote server structured to receive, store and jointly analyze a subset of the detection values from the monitoring devices. In embodiments, the analysis of the subset of detection values may include transitory signal analysis to identify the presence of high frequency torsional vibration. The monitoring application may be structured to subset detection values based on one of anticipated life of the rotating component, torsional vibration, type of the rotating component, operational conditions under which detection values were measured, and type of equipment. The analysis of the subset of detection values may include feeding a neural net with the subset of detection values and supplemental information to learn to recognize various operating states, health states, life expectancies and fault states utilizing deep learning techniques. The supplemental information may include one of component specification, component performance, equipment specification, equipment performance, maintenance records, repair records and an anticipated state model. The monitoring device may include a response circuit to perform at least one operation in response to the anticipated life of the rotating component. The at least one operation may include one of ordering a replacement of the rotating component, scheduling replacement of the rotating component, and recommending alternatives to the rotating component.

[0917] In embodiments, a system for evaluating the health of a variable frequency motor in an industrial environment may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a data storage circuit structured to store specifications, system geometry, and anticipated state information for a plurality of rotating components, store historical component performance and buffer the plurality of detection values for a predetermined length of time; and a torsional analysis circuit structured to utilize transitory signal analysis to analyze the buffered detection values relative to the rotating component specifications and anticipated state information resulting in identification of torsional vibration; a system analysis circuit structured to utilize the torsional vibration and at least one of an anticipated state, historical data and a system geometry to identify a motor health parameter; and a communication module enabled to communicate the motor health parameter, the torsional vibrations and detection values to a remote server, wherein the detection values communicated are based partly on the motor health parameter and the torsional vibration; and a monitoring application on the remote server structured to receive, store and jointly analyze a subset of the detection values from the monitoring devices.

[0918] In embodiments, a system for data collection, processing, and torsional analysis of a rotating component in an industrial environment may comprise a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors, wherein the plurality of input sensors comprises at least one of an angular position sensor, an angular velocity sensor and an angular acceleration sensor positioned to measure the rotating component; a streaming circuit for streaming at least a subset of the acquired detection values to a remote learning system; and a remote learning system including a torsional analysis circuit structured to analyze the detection values relative to a machine-based understanding of the state of the at least one rotating component. The machine-based understanding may be developed based on a model of the rotating component that determines a state of the at least one rotating component based at least in part on the relationship of the behavior of the rotating component to an operating frequency of a component of the industrial machine. The state of the at least one rotating component may be at least one of an operating state, a health state, a predicted lifetime state and a fault state. The machine-based understanding may be developed based by providing inputs to a deep learning machine, wherein the inputs comprise a plurality of streams of detection values for a plurality of rotating components and a plurality of measured state values for the plurality of rotating components. The state of the at least one rotating component may be at least one of an operating state, a health state, a predicted lifetime state and a fault state.

[0919] In embodiments, information about the health or other status or state information of or regarding a component or piece of industrial equipment may be obtained by monitoring the condition of various components throughout a process. Monitoring may include monitoring the amplitude of a sensor signal measuring attributes such as temperature, humidity, acceleration, displacement and the like. An embodiment of a data monitoring device 9700 is shown in FIG. 101 and may include a plurality of sensors 9706 communicatively coupled to a controller 9702. The controller 9702 may include a data acquisition circuit 9704, a signal evaluation circuit 9708, a data storage circuit 9716 and a response circuit 9710. The signal evaluation circuit 9708 may comprise a circuit for detecting a fault in one or more sensors, or a set of sensors, such as an overload detection circuit 9712, a sensor fault detection circuit 9714, or both. Additionally, the signal evaluation circuit 9708 may optionally comprise one or more of a peak detection circuit, a phase detection circuit, a bandpass filter circuit, a frequency transformation circuit, a frequency analysis circuit, a phase lock loop circuit, a torsional analysis circuit, a bearing analysis circuit, and the like.

[0920] The plurality of sensors 9706 may be wired to ports on the data acquisition circuit 9704. The plurality of sensors 9706 may be wirelessly connected to the data acquisition circuit 9704. The data acquisition circuit 9704 may be able to access detection values corresponding to the output of at least one of the plurality of sensors 9706 where the sensors 9706 may be capturing data on different operational aspects of a piece of equipment or an operating component.

[0921] The selection of the plurality of sensors 9706 for a data monitoring device 9700 designed for a specific component or piece of equipment may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, resolution desired at various positions in a process or plant, reliability of the sensors, and the like. The impact of a failure, time response of a failure (e.g., warning time and/or off-nominal modes occurring before failure), likelihood of failure, and/or sensitivity required and/or difficulty to detection failure conditions may drive the extent to which a component or piece of equipment is monitored with more sensors and/or higher capability sensors being dedicated to systems where unexpected or undetected failure would be costly or have severe consequences.

[0922] Depending on the type of equipment, the component being measured, the environment in which the equipment is operating and the like, sensors 9706 may comprise, without limitation, one or more of the following: a vibration sensor, a thermometer, a hygrometer, a voltage sensor and/or a current sensor (for the component and/or other sensors measuring the component), an accelerometer, a velocity detector, a light or electromagnetic sensor (e.g., determining temperature, composition and/or spectral analysis, and/or object position or movement), an image sensor, a structured light sensor, a laser-based image sensor, a thermal imager, an acoustic wave sensor, a displacement sensor, a turbidity meter, a viscosity meter, a axial load sensor, a radial load sensor, a tri-axial sensor, an accelerometer, a speedometer, a tachometer, a fluid pressure meter, an air flow meter, a horsepower meter, a flow rate meter, a fluid particle detector, an optical (laser) particle counter, an ultrasonic sensor, an acoustical sensor, a heat flux sensor, a galvanic sensor, a magnetometer, a pH sensor, and the like, including, without limitation, any of the sensors described throughout this disclosure and the documents incorporated by reference.

[0923] The sensors 9706 may provide a stream of data over time that has a phase component, such as relating to acceleration or vibration, allowing for the evaluation of phase or frequency analysis of different operational aspects of a piece of equipment or an operating component. The sensors 9706 may provide a stream of data that is not conventionally phase-based, such as temperature, humidity, load, and the like. The sensors 9706 may provide a continuous or near continuous stream of data over time, periodic readings, event-driven readings, and/or readings according to a selected interval or schedule.

[0924] In embodiments, as illustrated in FIG. 101, the sensors 9706 may be part of the data monitoring device 9700, referred to herein in some cases as a data collector, which in some cases may comprise a mobile or portable data collector. In embodiments, as illustrated in FIGS. 102 and 103, one or more external sensors 9724, which are not explicitly part of a monitoring device 9718 but rather are new, previously attached to or integrated into the equipment or component, may be opportunistically connected to or accessed by the monitoring device 9718. The monitoring device may include a data acquisition circuit 9704, a signal evaluation circuit 9708, a data storage circuit 9716 and a response circuit 9710. The signal evaluation circuit 9708 may comprise an overload detection circuit 9712, a sensor fault detection circuit 9714, or both. Additionally, the signal evaluation circuit 9708 may optionally comprise one or more of a peak detection circuit, a phase detection circuit, a bandpass filter circuit, a frequency transformation circuit, a frequency analysis circuit, a phase lock loop circuit, a torsional analysis circuit, a bearing analysis circuit, and the like. The data acquisition circuit 9704 may include one or more input ports 9726.

[0925] The one or more external sensors 9724 may be directly connected to the one or more input ports 9726 on the data acquisition circuit 9704 of the controller 9720 or may be accessed by the data acquisition circuit 9704 wirelessly, such as by a reader, interrogator, or other wireless connection, such as over a short-distance wireless protocol. In embodiments, as shown in FIG. 103, a data acquisition circuit 9704 may further comprise a wireless communication circuit 9731. The data acquisition circuit 9704 may use the wireless communication circuit 9731 to access detection values corresponding to the one or more external sensors 9724 wirelessly or via a separate source or some combination of these methods.

[0926] In embodiments, the data storage circuit 9716 may be structured to store sensor specifications, anticipated state information and detected values. The data storage circuit 9716 may provide specifications and anticipated state information to the signal evaluation circuit 9708.

[0927] In embodiments, an overload detection circuit 9712 may detect sensor overload by comparing the detected value associated with the sensor with a detected value associated with a sensor having a greater range/lower resolution monitoring the same component/attribute. Inconsistencies in measured value may indicate that the higher resolution sensor may be overloaded. In embodiments, an overload detection circuit 9712 may detect sensor overload by evaluating consistency of sensor reading with readings from other sensor data (monitoring the same or different aspects of the component/piece of equipment. In embodiments, an overload detection circuit 9712 may detect sensor overload by evaluating data collected by other sensors to identify conditions likely to result in sensor overload (e.g., heat flux sensor data indicative of the likelihood of overloading a sensor in a given location, accelerometer data indicating a likelihood of overloading a velocity sensor, and the like). In embodiments, an overload detection circuit 9712 may detect sensor overload by identifying flat line output following a rising trend. In embodiments, an overload detection circuit 9712 may detect sensor overload by transforming the sensor data to frequency data, using for example a Fast Fourier Transform (FFT), and then looking for a “ski-jump” in the frequency data which may result from the data being clipped due to an overloaded sensor. A sensor fault detection circuit 9714 may identify failure of the sensor itself, sensor health, or potential concerns regarding validity of sensor data. Rate of value change may be used to identify failure of the sensor itself. For example, a sudden jump to a maximum output may indicate a failure in the sensor rather than an overload of the sensor. In embodiments, an overload detection circuit 9712 and/or a sensor fault detection circuit 9714 may utilize sensor specifications, anticipated state information, sensor models and the like in the identification of sensor overload, failure, error, invalid data, and the like. In embodiments, the overload detection circuit 9712 or the sensor fault detection circuit 9714 may use detection values from other sensors and output from additional components such as a peak detection circuit and/or a phase detection circuit and/or a bandpass filter circuit and/or a frequency transformation circuit and/or a frequency analysis circuit and/or a phase lock loop circuit and the like to identify potential sources for the identified sensor overload, sensor faults, sensor failure, or the like. Sources or factors involved in sensor overload may include limitations on sensor range, sensor resolution, and sensor sampling frequency. Sources of apparent sensor overload may be due to a range, resolution or sampling frequency of a multiplexor suppling detection values associated with the sensor. Sources of factors involved in apparent sensor faults or failures may include environmental conditions; for example, excessive heat or cold may be associated with damage to semiconductor-based sensors, which may result in erratic sensor data, failure of a sensor to produce data, data that appears out of the range of normal behavior (e.g., large, discrete jumps in temperature for a system that does not normally experience such changes). Surges in current and/or voltage may be associated with damage to electrically connected sensors with sensitive components. Excessive vibration may result in physical damage to sensitive components of a sensor such as wires and/or connectors. An impact, which may be indicated by sudden acceleration or acoustical data may result in physical damage to a sensor with sensitive components such as wires and/or connectors. A rapid increase in humidity in the environment surrounding a sensor or an absence of oxygen may indicate water damage to a sensor. A sudden absence of signal from a sensor may be indicative of sensor disconnection which may due to vibration, impact and the like. A sensor that requires power may run out of battery power or be disconnected from a power source. In embodiments, the overload detection circuit 9712 or the sensor fault detection circuit 9714 may output a sensor status where the sensor status may be one of sensor overload, sensor failure, sensor fault, sensor healthy, and the like. The sensor fault detection circuit 9714 may determine one of a sensor fault status and a sensor validity status.

[0928] In embodiments, as illustrated in FIG. 104, the data acquisition circuit 9722 may further comprise a multiplexer circuit 9731 as described elsewhere herein. Outputs from the multiplexer circuit 9731 may be utilized by the signal evaluation circuit 9708. The response circuit 9710 may have the ability to turn on or off portions of the multiplexor circuit 9731. The response circuit 9710 may have the ability to control the control channels of the multiplexor circuit 9731.

[0929] In embodiments, the response circuit 9710 may initiate a variety of actions based on the sensor status provided by the overload detection circuit 9712. The response circuit 9710 may continue using the sensor if the sensor status is “sensor healthy.” The response circuit 9710 may adjust a sensor scaling value (e.g., from 100 mV/gram to 10 mV/gram). The response circuit 9710 may increase an acquisition range for an alternate sensor. The response circuit 9710 may back sensor data out of previous calculations and evaluations such as bearing analysis, torsional analysis and the like. The response circuit 9710 may use projected or anticipated data (based on data acquired prior to overload/failure) in place of the actual sensor data for calculations and evaluations such as bearing analysis, torsional analysis and the like. The response circuit 9710 may issue an alarm. The response circuit 9710 may issue an alert that may comprise notification that the sensor is out of range together with information regarding the extent of the overload such as “overload range—data response may not be reliable and/or linear”, “destructive range—sensor may be damaged,” and the like. The response circuit 9710 may issue an alert where the alert may comprise information regarding the effect of sensor load such as “unable to monitor machine health” due to sensor overload/failure,” and the like.

[0930] In embodiments, the response circuit 9710 may cause the data acquisition circuit 9704 to enable or disable the processing of detection values corresponding to certain sensors based on the sensor statues described above. This may include switching to sensors having different response rates, sensitivity, ranges, and the like; accessing new sensors or types of sensors, accessing data from multiple sensors, recruiting additional data collectors (such as routing the collectors to a point of work, using routing methods and systems disclosed throughout this disclosure and the documents incorporated by reference) and the like. Switching may be undertaken based on a model, a set of rules, or the like. In embodiments, switching may be under control of a machine learning system, such that switching is controlled based on one or more metrics of success, combined with input data, over a set of trials, which may occur under supervision of a human supervisor or under control of an automated system. Switching may involve switching from one input port to another (such as to switch from one sensor to another). Switching may involve altering the multiplexing of data, such as combining different streams under different circumstances. Switching may involve activating a system to obtain additional data, such as moving a mobile system (such as a robotic or drone system), to a location where different or additional data is available (such as positioning an image sensor for a different view or positioning a sonar sensor for a different direction of collection) or to a location where different sensors can be accessed (such as moving a collector to connect up to a sensor that is disposed at a location in an environment by a wired or wireless connection). This switching may be implemented by changing the control signals for a multiplexor circuit 9731 and/or by turning on or off certain input sections of the multiplexor circuit 9731.

[0931] In embodiments, the response circuit 9710 may make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 9710 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like.

[0932] In embodiments, the response circuit 9710 may recommend maintenance at an upcoming process stop or initiate a maintenance call where the maintenance may include the replacement of the sensor with the same or an alternate type of sensor having a different response rate, sensitivity, range and the like. In embodiments, the response circuit 9710 may implement or recommend process changes—for example to lower the utilization of a component that is near a maintenance interval, operating off-nominally, or failed for purpose but still at least partially operational, to change the operating speed of a component (such as to put it in a lower-demand mode), to initiate amelioration of an issue (such as to signal for additional lubrication of a roller bearing set, or to signal for an alignment process for a system that is out of balance), and the like.

[0933] In embodiments, the signal evaluation circuit 9708 and/or the response circuit 9710 may periodically store certain detection values in the data storage circuit 9716 to enable the tracking of component performance over time. In embodiments, based on sensor status, as described elsewhere herein recently measured sensor data and related operating conditions such as RPMs, component loads, temperatures, pressures, vibrations or other sensor data of the types described throughout this disclosure in the data storage circuit 9716 to enable the backing out of overloaded/failed sensor data. The signal evaluation circuit 9708 may store data at a higher data rate for greater granularity in future processing, the ability to reprocess at different sampling rates, and/or to enable diagnosing or post-processing of system information where operational data of interest is flagged, and the like.

[0934] In embodiments as shown in FIGS. 105, 106, 107, and 108, a data monitoring system 9726 may include at least one data monitoring device 9728. At least one data monitoring device 9728 may include sensors 9706 and a controller 9730 comprising a data acquisition circuit 9704, a signal evaluation circuit 9708, a data storage circuit 9716, and a communication circuit 9732 to allow data and analysis to be transmitted to a monitoring application 9736 on a remote server 9734. The signal evaluation circuit 9708 may include at least an overload detection circuit 9712. The signal evaluation circuit 9708 may periodically share data with the communication circuit 9732 for transmittal to the remote server 9734 to enable the tracking of component and equipment performance over time and under varying conditions by a monitoring application 9736. Based on the sensor status, the signal evaluation circuit 9708 and/or response circuit 9710 may share data with the communication circuit 9732 for transmittal to the remote server 9734 based on the fit of data relative to one or more criteria. Data may include recent sensor data and additional data such as RPMs, component loads, temperatures, pressures, vibrations, and the like for transmittal. The signal evaluation circuit 9708 may share data at a higher data rate for transmittal to enable greater granularity in processing on the remote server.

[0935] In embodiments, as shown in FIG. 105, the communication circuit 9732 may communicate data directly to a remote server 9734. In embodiments as shown in FIG. 106, the communication circuit 9732 may communicate data to an intermediate computer 9738 which may include a processor 9740 running an operating system 9742 and a data storage circuit 9744.

[0936] In embodiments, as illustrated in FIGS. 107 and 108, a data collection system 9746 may have a plurality of monitoring devices 9728 collecting data on multiple components in a single piece of equipment, collecting data on the same component across a plurality of pieces of equipment, (both the same and different types of equipment) in the same facility as well as collecting data from monitoring devices in multiple facilities. A monitoring application 9736 on a remote server 9734 may receive and store one or more of detection values, timing signals and data coming from a plurality of the various monitoring devices 9728.

[0937] In embodiments, as shown in FIG. 107, the communication circuit 9732 may communicated data directly to a remote server 9734. In embodiments, as shown in FIG. 108, the communication circuit 9732 may communicate data to an intermediate computer 9738 which may include a processor 9740 running an operating system 9742 and a data storage circuit 9744. There may be an individual intermediate computer 9738 associated with each monitoring device 9728 or an individual intermediate computer 9738 may be associated with a plurality of monitoring devices 9728 where the intermediate computer 9738 may collect data from a plurality of data monitoring devices and send the cumulative data to the remote server 9734. Communication to the remote server 9734 may be streaming, batch (e.g., when a connection is available) or opportunistic.

[0938] The monitoring application 9736 may select subsets of the detection values to be jointly analyzed. Subsets for analysis may be selected based on a single type of sensor, component or a single type of equipment in which a component is operating. Subsets for analysis may be selected or grouped based on common operating conditions such as size of load, operational condition (e.g., intermittent, continuous), operating speed or tachometer, common ambient environmental conditions such as humidity, temperature, air or fluid particulate, and the like. Subsets for analysis may be selected based on the effects of other nearby equipment such as nearby machines rotating at similar frequencies, nearby equipment producing electromagnetic fields, nearby equipment producing heat, nearby equipment inducing movement or vibration, nearby equipment emitting vapors, chemicals or particulates, or other potentially interfering or intervening effects.

[0939] In embodiments, the monitoring application 9736 may analyze the selected subset. In an illustrative example, data from a single sensor may be analyzed over different time periods such as one operating cycle, several operating cycles, a month, a year, the life of the component or the like. Data from multiple sensors of a common type measuring a common component type may also be analyzed over different time periods. Trends in the data such as changing rates of change associated with start-up or different points in the process may be identified. Correlation of trends and values for different sensors may be analyzed to identify those parameters whose short-term analysis might provide the best prediction regarding expected sensor performance. This information may be transmitted back to the monitoring device to update sensor models, sensor selection, sensor range, sensor scaling, sensor sampling frequency, types of data collected and analyzed locally or to influence the design of future monitoring devices.

[0940] In embodiments, the monitoring application 9736 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for a plurality of sensors, operational history, historical detection values, sensor life models and the like for use analyzing the selected subset using rule-based or model-based analysis. The monitoring application 9736 may provide recommendations regarding sensor selection, additional data to collect, or data to store with sensor data. The monitoring application 9736 may provide recommendations regarding scheduling repairs and/or maintenance. The monitoring application 9736 may provide recommendations regarding replacing a sensor. The replacement sensor may match the sensor being replaced or the replacement sensor may have a different range, sensitivity, sampling frequency and the like.

[0941] In embodiments, the monitoring application 9736 may include a remote learning circuit structured to analyze sensor status data (e.g., sensor overload, sensor faults, sensor failure) together with data from other sensors, failure data on components being monitored, equipment being monitored, product being produced, and the like. The remote learning system may identify correlations between sensor overload and data from other sensors.

[0942] Clause 1: In embodiments, a monitoring system for data collection in an industrial environment, the monitoring system comprising: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors; a data storage circuit structured to store sensor specifications, anticipated state information and detected values; a signal evaluation circuit comprising: an overload identification circuit structured to determine a sensor overload status of at least one sensor in response to the plurality of detection values and at least one of anticipated state information and sensor specification; a sensor fault detection circuit structured to determine one of a sensor fault status and a sensor validity status of at least one sensor in response to the plurality of detection values and at least one of anticipated state information and sensor specification; and a response circuit structured to perform at least one operation in response to one of a sensor overload status, a sensor health status, and a sensor validity status. A monitoring system of clause 1, the system further comprising a mobile data collector for collecting data from the plurality of input sensors. 3. The monitoring system of clause 1, wherein the at least one operation comprises issuing an alert or an alarm. 4. The monitoring system of clause 1, wherein the at least one operation further comprises storing additional data in the data storage circuit. 5. The monitoring system of clause 1, the system further comprising a multiplexor (MUX) circuit. 6. The monitoring system of clause 5, wherein the at least one operation comprises at least one of enabling or disabling one or more portions of the multiplexer circuit and altering the multiplexer control lines. 7. The monitoring system of clause 5, the system further comprising at least two multiplexer (MUX) circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits. 8. The monitoring system of clause 7, the system further comprising a MUX control circuit structured to interpret a subset of the plurality of detection values and provide the logical control of the MUX and the correspondence of MUX input and detected values as a result, wherein the logic control of the MUX comprises adaptive scheduling of the multiplexer control lines. 9. A system for data collection, processing, and component analysis in an industrial environment comprising: a plurality of monitoring devices, each monitoring device comprising: a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors; a data storage for storing specifications and anticipated state information for a plurality of sensor types and buffering the plurality of detection values for a predetermined length of time; a signal evaluation circuit comprising: an overload identification circuit structured to determine a sensor overload status of at least one sensor in response to the plurality of detection values and at least one of anticipated state information and sensor specification; a sensor fault detection circuit structured to determine one of a sensor fault status and a sensor validity status of at least one sensor in response to the plurality of detection values and at least one of anticipated state information and sensor specification; and a response circuit structured to perform at least one operation in response to one of a sensor overload status, a sensor health status, and a sensor validity status; a communication circuit structured to communicate with a remote server providing one of the sensor overload status, the sensor health status, and the sensor validity status and a portion of the buffered detection values to the remote server; and a monitoring application on the remote server structured to: receive the at least one selected detection value and one of the sensor overload status, the sensor health status, and the sensor validity status; jointly analyze a subset of the detection values received from the plurality of monitoring devices; and recommend an action. 10. The system of clause 9, with at least one of the monitoring devices further comprising a mobile data collector for collecting data from the plurality of input sensors. 11. The system of clause 9, wherein the at least one operation comprises issuing an alert or an alarm. 12. The monitoring system of clause 9, wherein the at least one operation further comprises storing additional data in the data storage circuit. 13. The system of clause 9, with at least one of the monitoring devices further comprising further comprising a multiplexor (MUX) circuit. 14. The system of clause 13, wherein the at least one operation comprises at least one of enabling or disabling one or more portions of the multiplexer circuit and altering the multiplexer control lines. 15. The system of clause 9, at least one of the monitoring devices further comprising at least two multiplexer (MUX) circuits and the at least one operation comprises changing connections between the at least two multiplexer circuits. 16. The monitoring system of clause 15, the system further comprising a MUX control circuit structured to interpret a subset of the plurality of detection values and provide the logical control of the MUX and the correspondence of MUX input and detected values as a result, wherein the logic control of the MUX comprises adaptive scheduling of the multiplexer control lines. 17. The system of clause 9, wherein the monitoring application comprises a remote learning circuit structured to analyze sensor status data together sensor data and identify correlations between sensor overload and data from other systems. 18. The system of clause 9, the monitoring application structured to subset detection values based on one of the sensor overload status, the sensor health status, the sensor validity status, the anticipated life of a sensor associated with detection values, the anticipated type of the equipment associated with detection values, and operational conditions under which detection values were measured. 19. The system of clause 9, wherein the supplemental information comprises one of sensor specification, sensor historic performance, maintenance records, repair records and an anticipated state model. 20. The system of clause 19, wherein the analysis of the subset of detection values comprises feeding a neural net with the subset of detection values and supplemental information to learn to recognize various sensor operating states, health states, life expectancies and fault states utilizing deep learning techniques.

[0943] Referring to FIGS. 109 through 136, embodiments of the present disclosure, including those involving expert systems, self-organization, machine learning, artificial intelligence, and the like, may benefit from the use of a neural net, such as a neural net trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes. References to a neural net throughout this disclosure should be understood to encompass a wide range of different types of neural networks, machine learning systems, artificial intelligence systems, and the like, such as feed forward neural networks, radial basis function neural networks, self-organizing neural networks (e.g., Kohonen self-organizing neural networks), recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layered neural networks, convolutional neural networks, hybrids of neural networks with other expert systems (e.g., hybrid fuzzy logic—neural network systems), autoencoder neural networks, probabilistic neural networks, time delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, Hopfield neural networks, Boltzmann machine neural networks, self-organizing map (SOM) neural networks, learning vector quantization (LVQ) neural networks, fully recurrent neural networks, simple recurrent neural networks, echo state neural networks, long short-term memory neural networks, bi-directional neural networks, hierarchical neural networks, stochastic neural networks, genetic scale RNN neural networks, committee of machines neural networks, associative neural networks, physical neural networks, instantaneously trained neural networks, spiking neural networks, neocognitron neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, compositional pattern-producing neural networks, memory neural networks, hierarchical temporal memory neural networks, deep feed forward neural networks, gated recurrent unit (GRU) neural networks, auto encoder neural networks, variational auto encoder neural networks, de-noising auto encoder neural networks, sparse auto-encoder neural networks, Markov chain neural networks, restricted Boltzmann machine neural networks, deep belief neural networks, deep convolutional neural networks, deconvolutional neural networks, deep convolutional inverse graphics neural networks, generative adversarial neural networks, liquid state machine neural networks, extreme learning machine neural networks, echo state neural networks, deep residual neural networks, support vector machine neural networks, neural Turing machine neural networks, and/or holographic associative memory neural networks, or hybrids or combinations of the foregoing, or combinations with other expert systems, such as rule-based systems, model-based systems (including ones based on physical models, statistical models, flow-based models, biological models, biomimetic models, and the like).

[0944] In embodiments, the foregoing neural network may be configured to connect with a DAQ instrument and other data collectors that may receive analog signals from one or more sensors. The foregoing neural networks may also be configured to interface with, connect to, or integrate with expert systems that can be local and/or available through one or more cloud networks. In embodiments, FIGS. 110 through 136 depict exemplary neural networks and FIG. 109 depicts a legend showing the various components of the neural networks depicted throughout FIGS. 110 to 136. FIG. 109 depicts the various neural net components 10000, as depicted in cells 10002 for which there are assigned functions and requirements. In embodiments, the various neural net examples may include back fed data/sensor cells 10010, data/sensor cells 10012, noisy input cells, 10014, and hidden cells, 10018. The neural net components 10000 also include the other following cells 10002: probabilistic hidden cells 10020, spiking hidden cells 10022, output cells 10024, match input/output cell 10028, recurrent cell 10030, memory cell, 10032, different memory cell 10034, kernels 10038 and convolution or pool cells 10040.

[0945] In FIG. 110, a streaming data collection system 10050 may include a DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including sensor 10060, sensor 10062 and sensor 10064. The streaming data collection system 10050 may include a perceptron neural network 10070 that may connect to, integrate with, or interface with an expert system 10080. In FIG. 111, a streaming data collection system 10090 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10090 may include a feed forward neural network 10092 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 112, a streaming data collection system 10100 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10100 may include a radial basis neural network 10102 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 113, a streaming data collection system 10110 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10110 may include a deep feed forward neural network 10112 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 114, a streaming data collection system 10120 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10120 may include a recurrent neural network 10122 that may connect to, integrate with, or interface with the expert system 10080.

[0946] In FIG. 115, a streaming data collection system 10130 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10130 may include a long/short term neural network 10132 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 116, a streaming data collection system 10140 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10140 may include a gated recurrent neural network 10142 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 117, a streaming data collection system 10150 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10150 may include an auto encoder neural network 10152 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 118, a streaming data collection system 10160 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10160 may include a variational neural network 10162 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 119, a streaming data collection system 10170 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10170 may include a denoising neural network 10172 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 120, a streaming data collection system 10180 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10180 may include a sparse neural network 10182 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 121, a streaming data collection system 10190 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10190 may include a Markov chain neural network 10182 that may connect to, integrate with, or interface with the expert system 10080.

[0947] In FIG. 122, a streaming data collection system 10200 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10200 may include a Hopfield network neural network 10202 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 123, a streaming data collection system 10210 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10210 may include a Boltzmann machine neural network 10212 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 124, a streaming data collection system 10220 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10220 may include a restricted BM neural network 10222 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 125, a streaming data collection system 10230 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10230 may include a deep belief neural network 10232 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 126, a streaming data collection system 10240 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10240 may include a deep convolutional neural network 10242 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 127, a streaming data collection system 10250 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10250 may include a deconvolutional neural network 10242 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 128, a streaming data collection system 10260 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10260 may include a deep convolutional inverse graphics neural network 10262 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 129, a streaming data collection system 10270 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10270 may include a generative adversarial neural network 10272 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 130, a streaming data collection system 10280 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10280 may include a liquid state machine neural network 10282 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 131, a streaming data collection system 10290 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10290 may include an extreme learning machine neural network 10292 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 132, a streaming data collection system 10300 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10300 may include an echo state neural network 10302 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 133, a streaming data collection system 10310 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10310 may include a deep residual neural network 10312 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 134, a streaming data collection system 10320 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10320 may include a Kohonen neural network 10322 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 135, a streaming data collection system 10330 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10330 may include a support vector machine neural network 10332 that may connect to, integrate with, or interface with the expert system 10080. In FIG. 136, a streaming data collection system 10340 may include the DAQ instrument 10052 or other data collectors that may gather analog signals from sensors including the sensors 10060, 10062, 10064. The streaming data collection system 10340 may include a neural Turing machine neural network 10342 that may connect to, integrate with, or interface with the expert system 10080.

[0948] The foregoing neural networks may have a variety of nodes or neurons, which may perform a variety of functions on inputs, such as inputs received from sensors or other data sources, including other nodes. Functions may involve weights, features, feature vectors, and the like. Neurons may include perceptrons, neurons that mimic biological functions (such as of the human senses of touch, vision, taste, hearing, and smell), and the like. Continuous neurons, such as with sigmoidal activation, may be used in the context of various forms of neural net, such as where back propagation is involved.

[0949] In many embodiments, an expert system or neural network may be trained, such as by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data (including the many types described throughout this disclosure), as well as one or more indicators of an outcome, such as an outcome of a process, an outcome of a calculation, an outcome of an event, an outcome of an activity, or the like. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.

[0950] In embodiments, a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more industrial environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission. In the cloud platform, optionally using massively parallel computational capability, a plurality of different neural networks of several types (including modular forms, structure-adaptive forms, hybrids, and the like) may be used to undertake prediction, classification, control functions, and provide other outputs as described in connection with expert systems disclosed throughout this disclosure. The different neural networks may be structured to compete with each other (optionally including the use of evolutionary algorithms, genetic algorithms, or the like), such that an appropriate type of neural network, with appropriate input sets, weights, node types and functions, and the like, may be selected, such as by an expert system, for a specific task involved in a given context, workflow, environment process, system, or the like.

[0951] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed forward neural network, which moves information in one direction, such as from a data input, like an analog sensor located on or proximal to an industrial machine, through a series of neurons or nodes, to an output. Data may move from the input nodes to the output nodes, optionally passing through one or more hidden nodes, without loops. In embodiments, feedforward neural networks may be constructed with various types of units, such as binary McCulloch-Pitts neurons, the simplest of which is a perceptron.

[0952] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, which may be preferred in some situations involving interpolation in a multi-dimensional space (such as where interpolation is helpful in optimizing a multi-dimensional function, such as for optimizing a data marketplace as described here, optimizing the efficiency or output of a power generation system, a factory system, or the like, or other situation involving multiple dimensions). In embodiments, each neuron in the RBF neural network stores an example from a training set as a “prototype.” Linearity involved in the functioning of this neural network offers RBF the advantage of not typically suffering from problems with local minima or maxima.

[0953] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, such as one that employs a distance criterion with respect to a center (e.g., a Gaussian function). A radial basis function may be applied as a replacement for a hidden layer (such as a sigmoidal hidden layer transfer) in a multi-layer perceptron. An RBF network may have two layers, such as the case where an input is mapped onto each RBF in a hidden layer. In embodiments, an output layer may comprise a linear combination of hidden layer values representing, for example, a mean predicted output. The output layer value may provide an output that is the same as or similar to that of a regression model in statistics. In classification problems, the output layer may be a sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is often improved by shrinkage techniques, such as ridge regression in classical statistics. This corresponds to a prior belief in small parameter values (and therefore smooth output functions) in a Bayesian framework. RBF networks may avoid local minima, because the only parameters that are adjusted in the learning process are the linear mapping from hidden layer to output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. In regression problems, this can be found in one matrix operation. In classification problems, the fixed non-linearity introduced by the sigmoid output function may be handled using an iteratively re-weighted least squares function or the like.

[0954] RBF networks may use kernel methods such as support vector machines (SVM) and Gaussian processes (where the RBF is the kernel function). A non-linear kernel function may be used to project the input data into a space where the learning problem can be solved using a linear model.

[0955] In embodiments, an RBF neural network may include an input layer, a hidden layer, and a summation layer. In the input layer, one neuron appears in the input layer for each predictor variable. In the case of categorical variables, N−1 neurons are used, where N is the number of categories. The input neurons may, in embodiments, standardize the value ranges by subtracting the median and dividing by the interquartile range. The input neurons may then feed the values to each of the neurons in the hidden layer. In the hidden layer, a variable number of neurons may be used (determined by the training process). Each neuron may consist of a radial basis function that is centered on a point with as many dimensions as a number of predictor variables. The spread (e.g., radius) of the RBF function may be different for each dimension. The centers and spreads may be determined by training. When presented with a vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron's center point and then apply the RBF kernel function to this distance, such as using the spread values. The resulting value may then be passed to the summation layer. In the summation layer, the value coming out of a neuron in the hidden layer may be multiplied by a weight associated with the neuron and may add to the weighted values of other neurons. This sum becomes the output. For classification problems, one output is produced (with a separate set of weights and summation units) for each target category. The value output for a category is the probability that the case being evaluated has that category. In training of an RBF, various parameters may be determined, such as the number of neurons in a hidden layer, the coordinates of the center of each hidden-layer function, the spread of each function in each dimension, and the weights applied to outputs as they pass to the summation layer. Training may be used by clustering algorithms (such as k-means clustering), by evolutionary approaches, and the like.

[0956] In embodiments, a recurrent neural network may have a time-varying, real-valued (more than just zero or one) activation (output). Each connection may have a modifiable real-valued weight. Some of the nodes are called labeled nodes, some output nodes, and others hidden nodes. For supervised learning in discrete time settings, training sequences of real-valued input vectors may become sequences of activations of the input nodes, one input vector at a time. At each time step, each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections. The system can explicitly activate (independent of incoming signals) some output units at certain time steps.

[0957] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing neural network, such as a Kohonen self-organizing neural network, such as for visualization of views of data, such as low-dimensional views of high-dimensional data. The self-organizing neural network may apply competitive learning to a set of input data, such as from one or more sensors or other data inputs from or associated with an industrial machine. In embodiments, the self-organizing neural network may be used to identify structures in data, such as unlabeled data, such as in data sensed from a range of vibration, acoustic, or other analog sensors in an industrial environment, where sources of the data are unknown (such as where vibrations may be coming from any of a range of unknown sources). The self-organizing neural network may organize structures or patterns in the data, such that they can be recognized, analyzed, and labeled, such as identifying structures as corresponding to vibrations induced by the movement of a floor, or acoustic signals created by high frequency rotation of a shaft of a somewhat distant machine.

[0958] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a recurrent neural network, which may allow for a bi-directional flow of data, such as where connected units (e.g., neurons or nodes) form a directed cycle. Such a network may be used to model or exhibit dynamic temporal behavior, such as those involved in dynamic systems including a wide variety of the industrial machines and devices described throughout this disclosure, such as a power generation machine operating at variable speeds or frequencies in variable conditions with variable inputs, a robotic manufacturing system, a refining system, or the like, where dynamic system behavior involves complex interactions that an operator may desire to understand, predict, control and/or optimize. For example, the recurrent neural network may be used to anticipate the state (such as a maintenance state, a fault state, an operational state, or the like), of an industrial machine, such as one performing a dynamic process or action. In embodiments, the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and/or from sensors and other data inputs from the industrial environment, of the various types described herein. In embodiments, the recurrent neural network may also be used for pattern recognition, such as for recognizing an industrial machine based on a sound signature, a heat signature, a set of feature vectors in an image, a chemical signature, or the like. In a non-limiting example, a recurrent neural network may recognize a shift in an operational mode of a turbine, a generator, a motor, a compressor, or the like (such as a gear shift) by learning to classify the shift from a training data set consisting of a stream of data from tri-axial vibration sensors and/or acoustic sensors applied to one or more of such machines.

[0959] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a modular neural network, which may comprise a series of independent neural networks (such as ones of various types described herein) that are moderated by an intermediary. Each of the independent neural networks in the modular neural network may work with separate inputs, accomplishing subtasks that make up the task the modular network as whole is intended to perform. For example, a modular neural network may comprise a recurrent neural network for pattern recognition, such as to recognize what type of industrial machine is being sensed by one or more sensors that are provided as input channels to the modular network and an RBF neural network for optimizing the behavior of the machine once understood. The intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such an appropriate control parameter, a prediction of state, or the like.

[0960] Combinations among any of the pairs, triplets, or larger combinations, of the various neural network types described herein, are encompassed by the present disclosure. This may include combinations where an expert system uses one neural network for recognizing a pattern (e.g., a pattern indicating a problem or fault condition) and a different neural network for self-organizing an activity or workflow based on the recognized pattern (such as providing an output governing autonomous control of a system in response to the recognized condition or pattern). This may also include combinations where an expert system uses one neural network for classifying an item (e.g., identifying a machine, a component, or an operational mode) and a different neural network for predicting a state of the item (e.g., a fault state, an operational state, an anticipated state, a maintenance state, or the like). Modular neural networks may also include situations where an expert system uses one neural network for determining a state or context (such as a state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, or the like) and a different neural network for self-organizing a process involving the state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, a boring process, or other process described herein).

[0961] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a physical neural network where one or more hardware elements is used to perform or simulate neural behavior. In embodiments, one or more hardware neurons may be configured to stream voltage values that represent analog vibration sensor data voltage values, to calculate velocity information from analog sensor inputs representing acoustic, vibration or other data, to calculation acceleration information from sensor inputs representing acoustic, vibration, or other data, or the like. One or more hardware nodes may be configured to stream output data resulting from the activity of the neural net. Hardware nodes, which may comprise one or more chips, microprocessors, integrated circuits, programmable logic controllers, application-specific integrated circuits, field-programmable gate arrays, or the like, may be provided to optimize the speed, input/output efficiency, energy efficiency, signal to noise ratio, or other parameter of some part of a neural net of any of the types described herein. Hardware nodes may include hardware for acceleration of calculations (such as dedicated processors for performing basic or more sophisticated calculations on input data to provide outputs, dedicated processors for filtering or compressing data, dedicated processors for decompressing data, dedicated processors for compression of specific file or data types (e.g., for handling image data, video streams, acoustic signals, vibration data, thermal images, heat maps, or the like), and the like. A physical neural network may be embodied in a data collector, such as a mobile data collector described herein, including one that may be reconfigured by switching or routing inputs in varying configurations, such as to provide different neural net configurations within the data collector for handling different types of inputs (with the switching and configuration optionally under control of an expert system, which may include a software-based neural net located on the data collector or remotely). A physical, or at least partially physical, neural network may include physical hardware nodes located in a storage system, such as for storing data within an industrial machine or in an industrial environment, such as for accelerating input/output functions to one or more storage elements that supply data to or take data from the neural net. A physical, or at least partially physical, neural network may include physical hardware nodes located in a network, such as for transmitting data within, to or from an industrial environment, such as for accelerating input/output functions to one or more network nodes in the net, accelerating relay functions, or the like. In embodiments of a physical neural network, an electrically adjustable resistance material may be used for emulating the function of a neural synapse. In embodiments, the physical hardware emulates the neurons, and software emulates the neural network between the neurons. In embodiments, neural networks complement conventional algorithmic computers. They are versatile and can be trained to perform appropriate functions without the need for any instructions, such as classification functions, optimization functions, pattern recognition functions, control functions, selection functions, evolution functions, and others.

[0962] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a multilayered feed forward neural network, such as for complex pattern classification of one or more items, phenomena, modes, states, or the like. In embodiments, a multilayered feedforward neural network may be trained by an optimization technical, such as a genetic algorithm, such as to explore a large and complex space of options to find an optimum, or near-optimum, global solution. For example, one or more genetic algorithms may be used to train a multilayered feedforward neural network to classify complex phenomena, such as to recognize complex operational modes of industrial machines, such as modes involving complex interactions among machines (including interference effects, resonance effects, and the like), modes involving non-linear phenomena, such as impacts of variable speed shafts, which may make analysis of vibration and other signals difficult, modes involving critical faults, such as where multiple, simultaneous faults occur, making root cause analysis difficult, and others. In embodiments, a multilayered feed forward neural network may be used to classify results from ultrasonic monitoring or acoustic monitoring of an industrial machine, such as monitoring an interior set of components within a housing, such as motor components, pumps, valves, fluid handling components, and many others, such as in refrigeration systems, refining systems, reactor systems, catalytic systems, and others.

[0963] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feedforward, back-propagation multi-layer perceptron (MLP) neural network, such as for handling one or more remote sensing applications, such as for taking inputs from sensors distributed throughout various industrial environments. In embodiments, the MLP neural network may be used for classification of physical environments, such as mining environments, exploration environments, drilling environments, and the like, including classification of geological structures (including underground features and above ground features), classification of materials (including fluids, minerals, metals, and the like), and other problems. This may include fuzzy classification.

[0964] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a structure-adaptive neural network, where the structure of a neural network is adapted, such as based on a rule, a sensed condition, a contextual parameter, or the like. For example, if a neural network does not converge on a solution, such as classifying an item or arriving at a prediction, when acting on a set of inputs after some amount of training, the neural network may be modified, such as from a feedforward neural network to a recurrent neural network, such as by switching data paths between some subset of nodes from unidirectional to bi-directional data paths. The structure adaptation may occur under control of an expert system, such as to trigger adaptation upon occurrence of a trigger, rule or event, such as recognizing occurrence of a threshold (such as an absence of a convergence to a solution within a given amount of time) or recognizing a phenomenon as requiring different or additional structure (such as recognizing that a system is varying dynamically or in a non-linear fashion). In one non-limiting example, an expert system may switch from a simple neural network structure like a feedforward neural network to a more complex neural network structure like a recurrent neural network, a convolutional neural network, or the like upon receiving an indication that a continuously variable transmission is being used to drive a generator, turbine, or the like in a system being analyzed.

[0965] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an autoencoder, autoassociator or Diabolo neural network, which may be similar to a multilayer perceptron (“MLP”) neural network, such as where there may be an input layer, an output layer and one or more hidden layers connecting them. However, the output layer in the auto-encoder may have the same number of units as the input layer, where the purpose of the MLP neural network is to reconstruct its own inputs (rather than just emitting a target value). Therefore, the auto encoders may operate as an unsupervised learning model. An auto encoder may be used, for example, for unsupervised learning of efficient codings, such as for dimensionality reduction, for learning generative models of data, and the like. In embodiments, an auto-encoding neural network may be used to self-learn an efficient network coding for transmission of analog sensor data from an industrial machine over one or more networks. In embodiments, an auto-encoding neural network may be used to self-learn an efficient storage approach for storage of streams of analog sensor data from an industrial environment.

[0966] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a probabilistic neural network (“PNN”), which in embodiments may comprise a multi-layer (e.g., four-layer) feedforward neural network, where layers may include input layers, hidden layers, pattern/summation layers and an output layer. In an embodiment of a PNN algorithm, a parent probability distribution function (PDF) of each class may be approximated, such as by a Parzen window and/or a non-parametric function. Then, using the PDF of each class, the class probability of a new input is estimated, and Bayes' rule may be employed, such as to allocate it to the class with the highest posterior probability. A PNN may embody a Bayesian network and may use a statistical algorithm or analytic technique, such as Kernel Fisher discriminant analysis technique. The PNN may be used for classification and pattern recognition in any of a wide range of embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict a fault condition of an engine based on collection of data inputs from sensors and instruments for the engine.

[0967] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a time delay neural network (TDNN), which may comprise a feedforward architecture for sequential data that recognizes features independent of sequence position. In embodiments, to account for time shifts in data, delays are added to one or more inputs, or between one or more nodes, so that multiple data points (from distinct points in time) are analyzed together. A time delay neural network may form part of a larger pattern recognition system, such as using a perceptron network. In embodiments, a TDNN may be trained with supervised learning, such as where connection weights are trained with back propagation or under feedback. In embodiments, a TDNN may be used to process sensor data from distinct streams, such as a stream of velocity data, a stream of acceleration data, a stream of temperature data, a stream of pressure data, and the like, where time delays are used to align the data streams in time, such as to help understand patterns that involve understanding of the various streams (e.g., where increases in pressure and acceleration occur as an industrial machine overheats).

[0968] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a convolutional neural network (referred to in some cases as a CNN, a ConvNet, a shift invariant neural network, or a space invariant neural network), wherein the units are connected in a pattern similar to the visual cortex of the human brain. Neurons may respond to stimuli in a restricted region of space, referred to as a receptive field. Receptive fields may partially overlap, such that they collectively cover the entire (e.g., visual) field. Node responses can be calculated mathematically, such as by a convolution operation, such as using multilayer perceptrons that use minimal preprocessing. A convolutional neural network may be used for recognition within images and video streams, such as for recognizing a type of machine in a large environment using a camera system disposed on a mobile data collector, such as on a drone or mobile robot. In embodiments, a convolutional neural network may be used to provide a recommendation based on data inputs, including sensor inputs and other contextual information, such as recommending a route for a mobile data collector. In embodiments, a convolutional neural network may be used for processing inputs, such as for natural language processing of instructions provided by one or more parties involved in a workflow in an environment. In embodiments, a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g., millions) parameters. A convolutional neural net may use one or more convolutional nets.

[0969] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a regulatory feedback network, such as for recognizing emergent phenomena (such as new types of faults not previously understood in an industrial environment).

[0970] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing map (“SOM”), involving unsupervised learning. A set of neurons may learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM may preserve these while mapping phenomena into groups.

[0971] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a learning vector quantization neural net (“LVQ”). Prototypical representatives of the classes may parameterize, together with an appropriate distance measure, in a distance-based classification scheme.

[0972] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an echo state network (“ESN”), which may comprise a recurrent neural network with a sparsely connected, random hidden layer. The weights of output neurons may be changed (e.g., the weights may be trained based on feedback). In embodiments, an ESN may be used to handle time series patterns, such as, in an example, recognizing a pattern of events associated with a gear shift in an industrial turbine, generator, or the like.

[0973] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a bi-directional, recurrent neural network (“BRNN”), such as using a finite sequence of values (e.g., voltage values from a sensor) to predict or label each element of the sequence based on both the past and the future context of the element. This may be done by adding the outputs of two RNNs, such as one processing the sequence from left to right, the other one from right to left. The combined outputs are the predictions of target signals, such as those provided by a teacher or supervisor. A bi-directional RNN may be combined with a long short-term memory RNN.

[0974] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical RNN that connects elements in various ways to decompose hierarchical behavior, such as into useful subprograms. In embodiments, a hierarchical RNN may be used to manage one or more hierarchical templates for data collection in an industrial environment.

[0975] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a stochastic neural network, which may introduce random variations into the network. Such random variations can be viewed as a form of statistical sampling, such as Monte Carlo sampling.

[0976] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a genetic scale recurrent neural network. In such embodiments, a RNN (often a LSTM) is used where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node. The outputs from all the various scales may be treated as a committee of members, and the associated scores may be used genetically for the next iteration.

[0977] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a committee of machines (“CoM”), comprising a collection of different neural networks that together “vote” on a given example. Because neural networks may suffer from local minima, starting with the same architecture and training, but using randomly different initial weights often gives different results. A CoM tends to stabilize the result.

[0978] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an associative neural network (“ASNN”), such as involving an extension of committee of machines that combines multiple feed forward neural networks and a k-nearest neighbor technique. It may use the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the bias of the neural network ensemble. An associative neural network may have a memory that can coincide with a training set. If new data become available, the network instantly improves its predictive ability and provides data approximation (self-learns) without retraining. Another important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models.

[0979] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an instantaneously trained neural network (“ITNN”), where the weights of the hidden and the output layers are mapped directly from training vector data.

[0980] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a spiking neural network, which may explicitly consider the timing of inputs. The network input and output may be represented as a series of spikes (such as a delta function or more complex shapes). SNNs can process information in the time domain (e.g., signals that vary over time, such as signals involving dynamic behavior of industrial machines). They are often implemented as recurrent networks.

[0981] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a dynamic neural network that addresses nonlinear multivariate behavior and includes learning of time-dependent behavior, such as transient phenomena and delay effects. Transients may include behavior of shifting industrial components, such as variable speeds of rotating shafts or other rotating components.

[0982] In embodiments, cascade correlation may be used as an architecture and supervised learning algorithm, supplementing adjustment of the weights in a network of fixed topology. Cascade-correlation may begin with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights may be frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The cascade-correlation architecture may learn quickly, determine its own size and topology, and retain the structures it has built even if the training set changes and requires no back-propagation.

[0983] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a neuro-fuzzy network, such as involving a fuzzy inference system in the body of an artificial neural network. Depending on the type, several layers may simulate the processes involved in a fuzzy inference, such as fuzzification, inference, aggregation and defuzzification. Embedding a fuzzy system in a general structure of a neural net as the benefit of using available training methods to find the parameters of a fuzzy system.

[0984] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a compositional pattern-producing network (“CPPN”), such as a variation of an associative neural network (“ANN”) that differs the set of activation functions and how they are applied. While typical ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs can include both types of functions and many others. Furthermore, CPPNs may be applied across the entire space of possible inputs, so that they can represent a complete image. Since they are compositions of functions, CPPNs in effect encode images at infinite resolution and can be sampled for a particular display at whatever resolution is optimal.

[0985] This type of network can add new patterns without re-training. In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a one-shot associative memory network, such as by creating a specific memory structure, which assigns each new pattern to an orthogonal plane using adjacently connected hierarchical arrays.

[0986] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical temporal memory (“HTM”) neural network, such as involving the structural and algorithmic properties of the neocortex. HTM may use a biomimetic model based on memory-prediction theory. HTM may be used to discover and infer the high-level causes of observed input patterns and sequences.

[0987] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a holographic associative memory (“HAM”) neural network, which may comprise an analog, correlation-based, associative, stimulus-response system. Information may be mapped onto the phase orientation of complex numbers. The memory is effective for associative memory tasks, generalization and pattern recognition with changeable attention.

[0988] In embodiments, various embodiments involving network coding may be used to code transmission data among network nodes in neural net, such as where nodes are located in one or more data collectors or machines in an industrial environment.

[0989] Clause 1. In embodiments, an expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising: a modular neural network, where the expert system uses one type of neural network for recognizing a pattern and a different neural network for self-organizing an activity in the industrial environment. 2. A system of clause 1, wherein the pattern indicates a fault condition of a machine. 3. A system of clause 1, wherein the self-organized activity governs autonomous control of a system in the environment. 4. A system of clause 3, wherein the expert system organizes the activity based at least in part on the recognized pattern. 5. An expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising:

• • [0990] a modular neural network, where the expert system uses one neural network for classifying an item and a different neural network for predicting a state of the item. 6. A system of clause 5, wherein classifying an item includes at least one of identifying a machine, a component, and an operational mode of a machine in the environment. 7. A system of clause 5, wherein predicting a state includes predicting at least one of a fault state, an operational state, an anticipated state, and a maintenance state. 8. An expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising: a modular neural network, where the expert system uses one neural network for determining at least one of a state and a context and a different neural network for self-organizing a process involving the at least one state or context. 9. A system of clause 8, wherein the stat or context includes at least one state of a machine, a process, a workflow, a marketplace, a storage system, a network, and a data collector. 10. A system of clause 8, wherein the self-organized process includes at least one of a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, and a boring process. 11. An expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising: a modular neural network, comprising at least two neural networks selected from the group consisting of feed forward neural networks, radial basis function neural networks, self-organizing neural networks, Kohonen self-organizing neural networks, recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layered neural networks, convolutional neural networks, a hybrids of a neural networks with another expert system, auto-encoder neural networks, probabilistic neural networks, time delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, Hopfield neural networks, Boltzmann machine neural networks, self-organizing map (“SOM”) neural networks, learning vector quantization (“LVQ”) neural networks, fully recurrent neural networks, simple recurrent neural networks, echo state neural networks, long short-term memory neural networks, bi-directional neural networks, hierarchical neural networks, stochastic neural networks, genetic scale RNN neural networks, committee of machines neural networks, associative neural networks, physical neural networks, instantaneously trained neural networks, spiking neural networks, neocognitron neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, compositional pattern-producing neural networks, memory neural networks, hierarchical temporal memory neural networks, deep feed forward neural networks, gated recurrent unit (“GRU”) neural networks, auto encoder neural networks, variational auto encoder neural networks, de-noising auto encoder neural networks, sparse auto-encoder neural networks, Markov chain neural networks, restricted Boltzmann machine neural networks, deep belief neural networks, deep convolutional neural networks, deconvolutional neural networks, deep convolutional inverse graphics neural networks, generative adversarial neural networks, liquid state machine neural networks, extreme learning machine neural networks, echo state neural networks, deep residual neural networks, support vector machine neural networks, neural Turing machine neural networks, and holographic associative memory neural networks. 12. A system for collecting data in an industrial environment, comprising
  • [0991] A physical neural network embodied in a mobile data collector, wherein the mobile data collector is adapted to be reconfigured by routing inputs in varying configurations, such that different neural net configurations are enabled within the data collector for handling different types of inputs. 13. A system of clause 12, wherein reconfiguration occurs under control of an expert system. 14. A system of clause 13, wherein the expert system includes a software-based neural net. 15. A system of clause 14, wherein the software-based system is located on the data collector. 16. A system of clause 14, wherein the software-based system is located remotely from the data collector. 17. A system for processing data collected from an industrial environment, the system comprising: a plurality of neural networks deployed in a cloud platform that receives data streams and other inputs collected from one or more industrial environments and transmitted to the cloud platform over one or more networks, wherein the neural networks are of different types. 18. A system of clause 17, wherein the plurality of neural networks includes at least one modular neural network. 19. A system of clause 17, wherein the plurality of neural networks includes at least one structure-adaptive neural network. 20. A system of clause 17, wherein the neural networks are structured to compete with each other under control of an expert system, such as by processing input data sets from the same industrial environment to provide outputs and comparing the outputs to at least one measure of success. 21. A system of clause 20, wherein a genetic algorithm is used to facilitate variation and selection for the competing neural networks. 22. A system of clause 20, wherein the measure of success includes at least one of the following measures: a measure of predictive accuracy, a measure of classification accuracy, an efficiency measure, a profit measure, a maintenance measure, a safety measure, and a yield measure. 23. A system, comprising: a network coding system for coding transmission of data among network nodes in neural network, wherein the nodes comprise hardware devices located in at least one of one or more data collectors, one or more storage systems, and one or more network devices located in an industrial environment.

[0992] Within the data collection, monitoring, and control environment of the industrial IoT are large and various sensor sets, which make efficient setup and timely changes to sensor data collection a challenge. Continuous collection from all sensors may be impossible given the large number of sensors and limited resources, such as limited availability of power and limited data collection and management facilities, including various limitations in availability and performance of sensor data collection devices, input/output interfaces, data transfer facilities, data storage, data analysis facilities, and the like. The number of sensors collected from at any given time must therefore be limited in an intelligent but timely manner, both at the time of setting up initial collection and during the process of collection, including handling rapid changes to a present collection scheme based on a change in state of a system, operational conditions (e.g., an alert condition, change in operational mode, etc.), or the like. Embodiments of the methods and systems disclosed herein may therefore include rapid route creation and modification for routing collectors, such as by taking advantage of hierarchical templates, execution of smart route changes, monitoring and responding to changes in operational conditions, and the like.

[0993] In embodiments, rapid route creation and modification for data collection in an industrial environment may take advantage of hierarchical templates. Templates may be used to take advantage of ‘like’ machinery that can utilize the same hierarchical sensor routing scheme. For example, among many possible types of machines about which data may be collected, the members of a certain class of motor, such as a stepper motor class, may have very similar sensor routing needs, such as for routine operations, routine maintenance, and failure mode detection, that may be described in a common hierarchy of sensor collection routines. The user installing a new stepper motor may then use the ‘stepper motor hierarchical routing template’ for the new motor. After installation, the stepper motor hierarchical routing template may then be used to change the routing schemes for changing conditions. The user may optionally make adjustments to the template as needed per unique motor functions, applications, environments, modes, and the like. The use of a template for deploying a routing scheme greatly reduces the time a user requires to configure the routing scheme for a new motor, or to deploy new routing technologies on an existing system that utilizes traditional sensor collection methods. Once the hierarchical routing template is in place, the sensor collection routine may be changed quickly based on the template, thus allowing for rapid route modification under changing conditions, such as: a change in the operating mode of the stepper motor that requires a different subset of sensors for monitoring, a limit alert or failure indication that requires a more focused subset of sensors for use in diagnosing the problem, and the like. Hierarchical routing templates thus allow for rapid deployment of sensor routing configurations, as well as allowing the sensed industrial environment to be altered dynamically as conditions change.

[0994] A functional hierarchy of routing templates may include different hierarchical configurations for a component, machine, system, industrial environment, and the like, including all sensors and a plurality of configurations formed from a subset of all sensors. At a system level, an ‘all-sensor’ configuration may include: a connection map to all sensors in a system, mapping to all onboard instrumentation sensors (e.g., monitoring points reporting within a machine or set of machines), mapping to an environment's sensors (e.g., monitoring points around the machines/equipment, but not necessarily onboard), mapping to available sensors on data collectors (e.g., data collectors that can be flexibly provisioned for particular data among different kinds), a unified map combining different individual mappings, and the like. A routing configuration may be provided, such as to indicate how to implement an operational routing scheme, a scheduled maintenance routing scheme (e.g., collecting from a greater set of overall sensors than in operational mode, but distributed across the system, or a focused sensor set for specific components, functions, and modes), one or more failure mode routing schemes for multiple focused sensor collection groups targeting different failure mode analyses (e.g., for a motor, one failure mode may be for bearings, another for startup speed-torque, where a different subset of sensor data is needed based on the failure mode, such as detected in anomalous readings taken during operations or maintenance), power savings (e.g., weather conditions necessitating reduced plant power), and the like.

[0995] As noted, hierarchical templates may also be conditional (e.g., rule-based), such as templates with conditional routing based on parameters, such as sensed data during a first collection period, where a subsequent routing configuration is varied. Within the hierarchy, nodes in a graph or tree may indicate forks by which conditional logic may be used, such as to select a given subset of sensors for a given operational mode. Thus, the hierarchical template may be associated with a rule-based or model-based expert system, which may facilitate automated routing based on the hierarchical template and based on observed conditions, such as based on a type of machine and its operational state, environmental context, or the like. In a non-limiting example, a hierarchical template may have an initial collection configuration and a conditional hierarchy in place to switch from the initial collection configuration to a second collection configuration based on the sensed conditions of an initial sensor collection. Continuing this example, among various possible machines, a conveyor system may have a plurality of sensors for collection in an initial collection, but once the first data is collected and analyzed, if the conveyor is determined to be in an idle state (such as due to the absence of a signal above a minimum threshold on a motion sensor), then the system may switch to a sensor data collection regime that is appropriate for the idles state of the conveyor (e.g., using a very small subset of the plurality of sensors, such as just using the motion sensor to detect departure from the idle state, at which point the original regime may be renewed and the rest of a sensor set may be re-engaged). Thus, when the collection of sensor data detects a changed condition to a state, an operational mode, an environmental condition, or the like, the sensor data collection may be switched to an appropriate configuration.

[0996] Hierarchical templates for one collector may be based on coordination of routing with that of other collectors. For instance, a collector might be set up to perform vibration analysis while another collector is set up to perform pressure or temperature on each machine in a set of similar machines, rather than having each machine collect all of the data on each machine, where otherwise setup for different sensor types may be required for each collector for each machine. Factors such as the duration of sampling required, the time required to set up a given sensor, the amount of power consumed, the time available for collection as a whole, the data rate of input/output of a sensor and/or the collector, the bandwidth of a channel (wired or wireless) available for transmission of collected data, and the like can be considered in arranging the coordination of the routing of two or more collectors, such that various parallel and serial configurations may be undertaken to achieve an overall effectiveness. This may include optimizing the coordination using an expert system, such as a rule-based optimization, a model-based optimization, or optimization using machine learning.

[0997] A machine learning system may create a hierarchical template structure for improved routing, such as for teaching the system the default operating conditions (e.g., normal operations mode, systems online and average production), peak operations mode (max capability), slack production, and the like. The machine learning system may create a new hierarchical template based on monitored conditions, such as a template based on a production level profile, a rate of production profile, a detected failure mode pattern analysis, and the like. The application of a new machine learning created template may be based on a mode matching between current production conditions and a machine learning template condition (e.g., the machine learning system creates a new template for a new production profile, and applies that new template whenever that new profile is detected).

[0998] Rapid route creation may be enabled using one or more hierarchical routing templates, such as when a routing template pre-establishes a routing scheme for different conditions, and when a trigger event executes a change in the sensor routing scheme to accommodate the condition. In embodiments, the trigger event may be an automatic change in routing based on a trigger that indicates a possible failure mode that forces a change in routing scheme from operational to failure mode analysis; a human-executed change in routing scheme based on received sensor data; a learned routing change based on machine learning of when to trigger a change (e.g., as based on a machine being fed with a set of human-executed or human-supervised changes); a manual routing change (e.g., optional to automatic/rapid automatic change); a human executed change based on observed device performance; and the like. Routing changes may include: changing from an operational mode to an accelerated maintenance, a failure mode analysis, a power saving mode a high-performance/high-output mode (e.g., for peak power in a generation plant), and the like.

[0999] Switching hierarchical template configurations may be executed based on connectivity with end-device sensors. In a highly automated collection routing environment (e.g., an indoor networked assembly plant) different routing collection configurations may be employed for fixed and flexible industrial layouts. In a fixed industrial layout, such as a layout with a high degree of wired connectivity between end-device sensors, automated collectors, and networks, there may be different routing configurations for a network routing hierarchy portion, a collector sensor-collection hierarchy portion, a storage portion, and the like. For a more flexible industrial layout with various wired and wireless connections between end-device sensors, automated collectors, and networks, there may be different schemes. For instance, a moderately automated collection routing environment may include: automatic collection and periodic network connection; a robot-carried collector for periodic collection (e.g., a ground-based robot, a drone, an underwater device, a robot with network connection, a robot with intermittent network connection, a robot that periodically uploads collection); a routing scheme with periodic collection and automated routing; a scheme only collecting periodically but routed directly upon collection; a routing scheme with periodic collection and periodic automated routing to collect periodically; and, over longer periods of time, periodically routing multiple collections; and the like. For a lower degree of automated collection routing, there may be a combination of: automatic collection and human-aided collectors (e.g., humans collecting alone, humans aided by robots), scheduled collection and human-aided collectors (e.g., humans initiating collection, humans aided by robots for collection initiation, human launching a drone to collect data at a remote site), and the like.

[1000] In embodiments, and referring to FIG. 137, hierarchical templates may be utilized by a local data collection system 10512 for collection and monitoring of data collected through a plurality of input channels 10500, such as data from sensors 10514, IoT devices 10516, and the like. The local collection system 10512, also referred to herein as a data collector 10512, may comprise a data storage 10502; a data acquisition circuit 10504; a data analysis circuit 10506; and the like, wherein the monitoring facilities may be deployed: locally on the data collector 10512; in part locally on the data collector and in part on a remote information technology infrastructure component apart from the data collector; and the like. A monitoring system may comprise a plurality of input channels communicatively coupled to the data collector 10512. The data storage 10502 may be structured to store a plurality of collector route templates 10510 and sensor specifications for sensors 10514 that correspond to the input channels 10500, wherein the plurality of collector route templates 10510 each comprise a different sensor collection routine. A data acquisition circuit 10504 may be structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels 10500, and a data analysis circuit 10506 structured to receive output data from the plurality of input channels 10500 and evaluate a current routing template collection routine based on the received output data, wherein the data collector 10512 is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the content of the output data. The monitoring system may further utilize a machine learning system (e.g., a neural network expert system), rule-based templates (e.g., based on an operational state of a machine with respect to which the input channels provide information, the input channels provide information, the input channels provide information), smart route changes, alarm states, network connectivity, self-organization amongst a plurality of data collectors, coordination of sensor groups, and the like.

[1001] In embodiments, evaluation of the current routing templates may be based on operational mode routing collection schemes, such as a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, a power saving operational mode, and the like. As a result of monitoring, the data collector may switch from a current routing template collection routine because the data analysis circuit determines a change in operating modes, such as the operating mode changing from an operational mode to an accelerated maintenance mode, the operating mode changing from an operational mode to a failure mode analysis mode, the operating mode changing from an operational mode to a power-saving mode, the operating mode changing from an operational mode to a high-performance mode, and the like. The data collector may switch from a current routing template collection routine based on a sensed change in a mode of operation, such as a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like. The evaluation of the current routing template collection routine may be based on a collection routine with respect to a collection parameter, such as network availability, sensor availability, a time-based collection routine (e.g., on a schedule, over time), and the like.

[1002] In embodiments, a monitoring system for data collection in an industrial environment may comprise: a data collector communicatively coupled to a plurality of input channels; a data storage structured to store a plurality of collector route templates and sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and a data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data, wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the content of the output data. In embodiments, the system is deployed locally on the data collector, in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, and the like. Each of the input channels may correspond to a sensor located in the environment. The evaluation of the current routing template may be based on operational mode routing collection schemes. The operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power saving operational mode. The data collector may switch from the current routing template collection routine because the data analysis circuit determines a change in operating modes, such as where the operating mode changes from an operational mode to an accelerated maintenance mode, from an operational mode to a failure mode analysis mode, from an operational mode to a power saving mode, from an operational mode to high-performance mode, and the like. The data collector may switch from the current routing template collection routine based on a sensed change in a mode of operation, such as where the sensed change is a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like. The evaluation of the current routing template collection routine may be based on a collection routine with respect to a collection parameter, such as where the parameter is network availability, sensor availability, a time-based collection routine (e.g., where a routine collects sensor data on a schedule, evaluates sensor data over time).

[1003] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a plurality of collector route templates and sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data, wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the content of the output data. In embodiments, the computer-implemented method is deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment.

[1004] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a plurality of collector route templates and sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data, wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the content of the output data. In embodiments, the instructions may be deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment.

[1005] In embodiments, a monitoring system for data collection in an industrial environment may comprise a data collector communicatively coupled to a plurality of input channels; a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and a machine learning data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data received over time, wherein the machine learning data analysis circuit learns received output data patterns, wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the learned received output data patterns. In embodiments, the monitoring system may be deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment. The machine learning data analysis circuit may include a neural network expert system. The evaluation of the current routing template may be based on operational mode routing collection schemes. The operational mode may be at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power saving operational mode. The data collector may switch from the current routing template collection routine because the data analysis circuit determines a change in operating modes, such as where the operating mode changes from an operational mode to an accelerated maintenance mode, from an operational mode to a failure mode analysis mode, from an operational mode to a power saving mode, from an operational mode to high-performance mode, and the like. The data collector may switch from the current routing template collection routine based on a sensed change in a mode of operation, such as where the sensed change is a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like. The evaluation of the current routing template collection routine may be based on a collection routine with respect to a collection parameter, such as where the parameter is network availability, a sensor availability, a time-based collection routine (collects sensor data on a schedule, evaluates sensor data over time).

[1006] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and providing a machine learning data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data received over time, wherein the machine learning data analysis circuit learns received output data patterns, wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the learned received output data patterns. In embodiments, the method may be deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment.

[1007] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and providing a machine learning data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data received over time, wherein the machine learning data analysis circuit learns received output data patterns, wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the learned received output data patterns. In embodiments, the instructions may be deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment.

[1008] In embodiments, a monitoring system for data collection in an industrial environment may comprise: a data collector communicatively coupled to a plurality of input channels; a data storage structured to store a collector route template, sensor specifications for sensors that correspond to the input channels, wherein the collector route template comprises a sensor collection routine; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and a data analysis circuit structured to receive output data from the plurality of input channels and evaluate the received output data with respect to a rule, wherein the data collector is configured to modify the sensor collection routine based on the application of the rule to the received output data. In embodiments, the system may be deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment. The rule may be based on an operational state of a machine with respect to which the input channels provide information, on an anticipated state of a machine with respect to which the input channels provide information, on a detected fault condition of a machine with respect to which the input channels provide information, and the like. The evaluation of the received output data may be based on operational mode routing collection schemes, where the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power saving operational mode. The data collector may modify the sensor collection routine because the data analysis circuit determines a change in operating modes, such as where the operating mode changes from an operational mode to an accelerated maintenance mode, from an operational mode to a failure mode analysis mode, from an operational mode to a power saving mode, from an operational mode to high-performance mode, and the like. The data collector may modify the sensor collection routine based on a sensed change in a mode of operation, such as where the sensed change is a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like. The evaluation of the received output data may be based on a collection routine with respect to a collection parameter, wherein the parameter is a network availability, a sensor availability, a time-based collection routine (e.g., collects sensor data on a schedule or over time), and the like.

[1009] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a collector route template, sensor specifications for sensors that correspond to the input channels, wherein the collector route template comprises a sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate the received output data with respect to a rule, wherein the data collector is configured to modify the sensor collection routine based on the application of the rule to the received output data. In embodiments, the method may be deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment.

[1010] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a collector route template, sensor specifications for sensors that correspond to the input channels, wherein the collector route template comprises a sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate the received output data with respect to a rule, wherein the data collector is configured to modify the sensor collection routine based on the application of the rule to the received output data. In embodiments, the instructions may be deployed locally on the data collector, such as deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, where each of the input channels correspond to a sensor located in the environment.

[1011] Rapid route creation and modification in an industrial environment may employ smart route changes based on incoming data or alarms, such as changes enabling dynamic selection of data collection for analysis or correlation. Smart route changes may enable the system to alter current routing of sensor data based on incoming data or alarms. For instance, a user may set up a routing configuration that establishes a schedule of sensor collection for analysis, but when the analysis (or an alarm) indicates a special need, the system may change the sensor routing to address that need. For example, in the case where a change in a motor vibration profile (as one example among any of the machines described throughout this disclosure), such as rapidly increasing the peak amplitude of shaking on at least one axis of a vibration sensor set, that indicates a potential early failure of the motor, the system may change the routing to collect more focused data collection for analysis, such as initiating collection on more axes of the motor, initiating collection on additional bearings of the motor, and/or initiating collection using other sensors (such as temperature or heat flux sensors), that may confirm an initial hypothesis that the failure mode is occurring or otherwise assist in analysis of the state or operational condition of the machine.

[1012] Detected operational mode changes may trigger a rapid route change. For instance, an operational mode may be detected as the result of a single-point sensor out-of-range detection, an analysis determination, and the like, and generate a routing change. An analysis determination may be detected from a sensor end-point, such as through a single-point sensor analysis, a multiple-point sensor analysis, an analysis domain analysis (e.g., through a time profile, frequency profile, correlated multi-point determination), and the like. In another instance, a maintenance mode may be detected during routine maintenance, where a routing change increases data collection to capture data at a higher rate under an anomalous condition. A failure mode may be detected, such as through an alarm that indicates near-term potential for a failure of a machine that triggers increased data capture rate for analysis. Performance-based modes may be detected, such as detecting a level of output rate (e.g., peak, slack, idle), which may then initiate changes in routing to accommodate the analysis needs for the different performance monitoring and metrics associated with the state. For example, if a high peak speed is detected for a motor, a conveyor, an assembly line, a generator, a turbine, or the like, relative to historical measurements over some time period, additional sensors may be engaged to watch for failures that are typically associated with peak speeds, such as overheating (as measured by engaging a temperature or heat flux sensor), excessive noise (as measured by an acoustic or noise sensor), excessive shaking (as measured by one or more vibration sensors), or the like.

[1013] Alarm detections may trigger a rapid route change. Alarm sources may include a front-end collector, local intelligence resource, back-end data analysis process, ambient environment detector, network quality detector, power quality detector, heat, smoke, noise, flooding, and the like. Alarm types may include a single-instance anomaly detection, multiple-instance anomaly detection, simultaneous multi-sensor detection, time-clustered sensor detection (e.g., a single sensor or multiple sensors), frequency-profile detection (e.g., increasing rate of anomaly detection such as an alarm increasing in its occurrence over time, a change in a frequency component of a sensor output such as a motor's physical vibration profile changing over time), and the like. [001003]A machine learning system may change routing based on learned alarm pattern analysis. The machine learning system may learn system alarm condition patterns, such as alarm conditions expected under normal operating conditions, under peak operating conditions, expected over time based on age of components (e.g., new, during operational life, during extended life, during a warrantee period), and the like. The machine learning system may change routing based on a change in an alarm pattern, such as a system operating normally but experiencing a peak operating alarm pattern (e.g., a system running when it should not be), a system is new but experiencing an older profile (e.g., detection of infant mortality), and the like. The machine learning system may change routing based on a current alarm profile vs. an expected change in production condition. For example, a plant, system, or component is experiencing above average alarm conditions just before a ramp-up of production (e.g., could be foretelling of above average failures during increased production), just before going slack (e.g., could be an opportunity to ramp up maintenance procedures based on increased data taking routing scheme), after an unplanned event (e.g., weather, power outage, restart), and the like.

[1014] A rapid route change action may include: an increased rate of sampling (e.g., to a single sensor, to multiple sensors), an increase in the number of sensors being sampled (e.g., simultaneous sampling of other sensors on a device, coordinated sampling of similar sensors on near-by devices), generating a burst of sampling (e.g., sampling at a high rate for a period of time), and the like. Actions may be executed on a schedule, coordinated with a trigger, based on an operational mode, and the like. Triggered actions may include: anomalous data, an exceeded threshold level, an operational event trigger (e.g., at startup condition such as for startup motor torque), and the like.

[1015] A rapid route change may switch between routing schemes, such as an operational routing scheme (e.g., a subset of sensor collection for normal operations), a scheduled maintenance routing scheme (e.g., an increased and focused set of sensor collection than for normal operations), and the like. The distribution of sensor data may be changed, such as to distribute sensor collection across the system, such as for a sensor collection set for specific components, functions, and modes. A failure mode routing scheme may entail multiple focused sensor collection groups targeting different failure mode analyses (e.g., for a motor, one failure mode may be for bearings, another for startup speed-torque) where a different subset of sensor data may be needed based on the failure mode (e.g., as detected in anomalous readings taken during operations or maintenance). Power saving mode routing may be executed when weather conditions necessitate reduced plant power.

[1016] Dynamic adjustment of route changes may be executed based on connectivity factors, such as the factors associated with the collector or network availability and bandwidth. For example, routing may be changed for a device associated with an alarm detection, where changing routing for targeted devices on the network frees up bandwidth. Changes to routing may have a duration, such as only for a pre-determined period of time and then switching back, maintaining a change until user-directed, changing duration based on network availability, and the like.

[1017] In embodiments, and referring to FIG. 139, smart route changes may be implemented by a local data collection system 10520 for collection and monitoring of data collected through a plurality of input channels 10500, such as data from sensors 10522, IoT devices 10524, and the like. The local collection system 10520, also referred to herein as a data collector 10520, may comprise a data storage 10502, a data acquisition circuit 10504, a data analysis circuit 10506, a response circuit 10508, and the like, wherein the monitoring facilities may be deployed locally on the data collector 10520, in part locally on the data collector and in part on a remote information technology infrastructure component apart from the data collector, and the like. Smart route changes may be implemented between data collectors, such as where a state message is transmitted between the data collectors (e.g., from an input channel that is mounted in proximity to a second input channel, from a related group of input sensors, and the like). A monitoring system may comprise a plurality of input channels 10500 communicatively coupled to the data collector 10520. The data acquisition circuit 10504 may be structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels 10500, wherein the data acquisition circuit 10504 acquires sensor data from a first route of input channels for the plurality of input channels. The data storage 10502 may be structured to store sensor data, sensor specifications, and the like, for sensors 10522 that correspond to the input channels 10500. The data analysis circuit 10506 may be structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information may include an alarm threshold level, and wherein the data analysis circuit 10506 sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels. Further, the data analysis circuit 10506 may transmit the alarm state across a network to a routing control facility. The response circuit 10508 may be structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels upon reception of a routing change indication from the routing control facility. In the case of a network transmission, the alternate routing of input channels may include the first input channel and a group of input channels related to the first input channel, where the data collector executes the change in routing of the input channels if a communication parameter of the network between the data collector and the routing control facility is not met (e.g., a time-period parameter, a network connection and/or bandwidth availability parameter).

[1018] In embodiments, an alarm state may indicate a detection mode, such as an operational mode detection comprising an out-of-range detection, a maintenance mode detection comprising an alarm detected during maintenance, a failure mode detection (e.g., where the controller communicates a failure mode detection facility), a power mode detection wherein the alarm state is indicative of a power related limitation data of the anticipated state information, a performance mode detection wherein the alarm state is indicative of a high-performance limitation data of the anticipated state information, and the like. The monitoring system may further include the analysis circuit setting the alarm state when the alarm threshold level is exceeded for an alternate input channel in the first group of input channels, such as where the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the second routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis. The second routing of input channels may include a change in a routing collection parameter, such as where the routing collection parameter is an increase in sampling rate, an increase in the number of channels being sampled, a burst sampling of at least one of the plurality of input channels, and the like.

[1019] In embodiments, and referring to FIG. 138, collector route templates 10510 may be utilized for smart route changes and may be implemented by a local data collection system 10512 for collection and monitoring of data collected through a plurality of input channels 10500, such as data from sensors 10514, IoT devices 10516, and the like. The local collection system 10512, also referred to herein as a data collector 10512, may comprise a data storage 10502, a data acquisition circuit 10504, a data analysis circuit 10506, a response circuit 10508, and the like, wherein the monitoring facilities may be deployed locally on the data collector 10512, in part locally on the data collector and in part on a remote information technology infrastructure component apart from the data collector, and the like.

[1020] In embodiments, a monitoring system for data collection in an industrial environment may comprise: a data collector communicatively coupled to a plurality of input channels; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; a data storage structured to store sensor specifications for sensors that correspond to the input channels; a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; and a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel. In embodiments, the system may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment. The group of input channels may be related to the first input channel are at least in part taken from the plurality of input channels not included in the first routing of input channels. An alarm state may indicate a detection mode, such as where the detection mode is an operational mode detection comprising an out-of-range detection, the detection mode is a maintenance mode detection comprising an alarm detected during maintenance, the detection mode is a failure mode detection. The controller may communicate the failure mode detection facility, such as where the detection mode is a power mode detection and the alarm state is indicative of a power related limitation data of the anticipated state information, the detection mode is a performance mode detection and the alarm state is indicative of a high-performance limitation data of the anticipated state information, and the like. The analysis circuit may set the alarm state when the alarm threshold level is exceeded for an alternate input channel in the first group of input channels, such as where the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis. The alternate routing of input channels may include a change in a routing collection parameter, such as for an increase in sampling rate, an increase in the number of channels being sampled, a burst sampling of at least one of the plurality of input channels, and the like.

[1021] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel. In embodiments, the system may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1022] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel. In embodiments, the instructions may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1023] In embodiments, a monitoring system for data collection in an industrial environment may comprise: a data collector communicatively coupled to a plurality of input channels; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; a data storage structured to store sensor specifications for sensors that correspond to the input channels; a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels and transmits the alarm state across a network to a routing control facility; and a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels upon reception of a routing change indication from the routing control facility, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel, wherein the data collector automatically executes the change in routing of the input channels if a communication parameter of the network between the data collector and the routing control facility is not met. In embodiments, the instructions may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment. The communication parameter may be a time-period parameter within which the routing control facility must respond. The communication parameter may be a network availability parameter, such as a network connection parameter or bandwidth requirement. The group of input channels related to the first input channel may be at least in part taken from the plurality of input channels not included in the first routing of input channels. The alarm state may indicate a detection mode, such as an operational mode detection comprising an out-of-range detection, a maintenance mode detection comprising an alarm detected during maintenance, and the like. The detection mode may be a failure mode detection, such as when the controller communicates the failure mode detection facility, the alarm state is indicative of a power related limitation data of the anticipated state information, the detection mode is a performance mode detection where the alarm state is indicative of a high-performance limitation data of the anticipated state information, and the like. The analysis circuit may set the alarm state when the alarm threshold level is exceeded for an alternate input channel in the first group of input channels, such as where the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis. The alternate routing of input channels may be a change in a routing collection parameter, such as an increase in sampling rate, is an increase in the number of channels being sampled, a burst sampling of at least one of the plurality of input channels, and the like.

[1024] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels and transmits the alarm state across a network to a routing control facility; and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels upon reception of a routing change indication from the routing control facility, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel, wherein the data collector automatically executes the change in routing of the input channels if a communication parameter of the network between the data collector and the routing control facility is not met. In embodiments, the instructions may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1025] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising: providing a data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels and transmits the alarm state across a network to a routing control facility; and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels upon reception of a routing change indication from the routing control facility, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel, wherein the data collector automatically executes the change in routing of the input channels if a communication parameter of the network between the data collector and the routing control facility is not met. In embodiments, the instructions may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1026] In embodiments, a monitoring system for data collection in an industrial environment may comprise: a first and second data collector communicatively coupled to a plurality of input channels; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; a data storage structured to store sensor specifications for sensors that correspond to the input channels; a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; a communication circuit structured to communicate with a second data collector, wherein the second data collector transmits a state message related to a first input channel from the first route of input channels; and a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels based on the state message from the second data collector, wherein the alternate routing of input channel comprise the first input channel and a group of input channels related to the first input sensor. In embodiments, the system may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment. The set state message transmitted from the second data collector may be from a second input channel that is mounted in proximity to the first input channel. The set alarm transmitted from the second controller may be from a second input sensor that is part of a related group of input sensors comprising the first input sensor. The group of input channels related to the first input channel may be at least in part taken from the plurality of input channels not included in the first routing of input channels. The alarm state may indicate a detection mode, such as where the detection mode is an operational mode detection comprising an out-of-range detection, a maintenance mode detection comprising an alarm detected during maintenance, is a failure mode detection, and the like. The controller may communicate the failure mode detection facility, such as where the detection mode is a power mode detection and the alarm state is indicative of a power related limitation data of the anticipated state information, the detection mode is a performance mode detection where the alarm state is indicative of a high-performance limitation data of the anticipated state information, and the like. The analysis circuit may set the alarm state when the alarm threshold level is exceeded for an alternate input channel in the first group of input channels, such as where the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis. The alternate routing of input channels may be a change in a routing collection parameter, such as an increase in sampling rate, an increase in the number of channels being sampled, a burst sampling of at least one of the plurality of input channels, and the like.

[1027] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a first and second data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; providing a communication circuit structured to communicate with a second data collector, wherein the second data collector transmits a state message related to a first input channel from the first route of input channels, and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels based on the state message from the second data collector, wherein the alternate routing of input channel comprise the first input channel and a group of input channels related to the first input sensor. In embodiments, the method may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1028] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising: providing a first and second data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; providing a communication circuit structured to communicate with a second data collector, wherein the second data collector transmits a state message related to a first input channel from the first route of input channels, and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels based on the state message from the second data collector, wherein the alternate routing of input channel comprise the first input channel and a group of input channels related to the first input sensor. In embodiments, the instructions may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1029] In embodiments, a monitoring system for data collection in an industrial environment may comprise: a data collector communicatively coupled to a plurality of input channels; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channel, wherein the data acquisition circuit acquires sensor data from a first group of input channels from the plurality of input channels; a data storage structured to store sensor specifications for sensors that correspond to the input channels; a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channel; and a response circuit structured to change the input channels being collected from the first group of input channels to an alternative group of input channels, wherein the alternate group of input channels comprise the first input channel and a group of input channels related to the first input sensor. In embodiments, the system may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment. The group of input sensors related to the first input sensor may be at least in part taken from the plurality of input sensors not included in the first group of input sensors. The first group of input channels related to the first input channel may be at least in part taken from the plurality of input channels not included in the first routing of input channels. The alarm state may indicate a detection mode, such as where the detection mode is an operational mode detection comprising an out-of-range detection, a maintenance mode detection comprising an alarm detected during maintenance. The detection mode may be a failure mode detection, such as where the controller communicates the failure mode detection facility. The detection mode may be a power mode detection where the alarm state is indicative of a power related limitation data of the anticipated state information. The detection mode may be a performance mode detection, where the alarm state is indicative of a high-performance limitation data of the anticipated state information. The analysis circuit may set the alarm state when the alarm threshold level is exceeded for an alternate input channel in the first group of input channels, such as when the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis. An alternative group of input channels may include a change in a routing collection parameter, such as where the routing collection parameter is an increase in sampling rate, an increase in the number of channels being sampled, a burst sampling of at least one of the plurality of input channels, and the like.

[1030] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channel, wherein the data acquisition circuit acquires sensor data from a first group of input channels from the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channel; and providing a response circuit structured to change the input channels being collected from the first group of input channels to an alternative group of input channels, wherein the alternate group of input channels comprise the first input channel and a group of input channels related to the first input sensor. In embodiments, the method may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1031] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising: providing a data collector communicatively coupled to a plurality of input channels; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channel, wherein the data acquisition circuit acquires sensor data from a first group of input channels from the plurality of input channels; providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channel; and providing a response circuit structured to change the input channels being collected from the first group of input channels to an alternative group of input channels, wherein the alternate group of input channels comprise the first input channel and a group of input channels related to the first input sensor. In embodiments, the instructions may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1032] In embodiments, a monitoring system for data collection in an industrial environment may comprise: a data collector communicatively coupled to a plurality of input channels; a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels; and a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels, wherein the data collector is configured to switch from a current routing template collection routine to an alternate routing template collection routine based on a setting of an alarm state. In embodiments, the system may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment. The setting of the alarm state may be based on operational mode routing collection schemes, such as where the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power saving operational mode. The alarm threshold level may be associated with a sensed change to one of the plurality of input channels, such as where the sensed change is a failure condition, is a performance condition, a power condition, a temperature condition, a vibration condition, and the like. The alarm state may indicate a detection mode, such as where the detection mode is an operational mode detection comprising an out-of-range detection, a maintenance mode detection comprising an alarm detected during maintenance, and the like. The detection mode may be a power mode detection, where the alarm state is indicative of a power related limitation data of the anticipated state information. The detection mode may be a performance mode detection, where the alarm state is indicative of a high-performance limitation data of the anticipated state information. The analysis circuit may set the alarm state when the alarm threshold level is exceeded for an alternate input channel, such as wherein the setting of the alarm state is determined to be a multiple-instance anomaly detection. The alternate routing template may be a change to an input channel routing collection parameter. The routing collection parameter may be an increase in sampling rate, such as an increase in the number of channels being sampled, a burst sampling of at least one of the plurality of input channels, and the like.

[1033] In embodiments, a computer-implemented method for implementing a monitoring system for data collection in an industrial environment may comprise: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels; and providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels, wherein the data collector is configured to switch from a current routing template collection routine to an alternate routing template collection routine based on a setting of an alarm state. In embodiments, the system may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1034] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising: providing a data collector communicatively coupled to a plurality of input channels; providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine; providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels; and providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels, wherein the data collector is configured to switch from a current routing template collection routine to an alternate routing template collection routine based on a setting of an alarm state. In embodiments, the instructions may be deployed locally on the data collector, deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector, wherein each of the input channels correspond to a sensor located in the environment.

[1035] Methods and systems are disclosed herein for a system for data collection in an industrial environment using intelligent management of data collection bands, referred to herein in some cases as smart bands. Smart bands may facilitate intelligent, situational, context-aware collection of data, such as by a data collector (such as any of the wide range of data collector embodiments described throughout this disclosure). Intelligent management of data collection via smart bands may improve various parameters of data collection, as well as parameters of the processes, applications, and products that depend on data collection, such as data quality parameters, consistency parameters, efficiency parameters, comprehensiveness parameters, reliability parameters, effectiveness parameters, storage utilization parameters, yield parameters (including financial yield, output yield, and reduction of adverse events), energy consumption parameters, bandwidth utilization parameters, input/output speed parameters, redundancy parameters, security parameters, safety parameters, interference parameters, signal-to-noise parameters, statistical relevancy parameters, and others. Intelligent management of smart bands may optimize across one or more such parameters, such as based on a weighting of the value of the parameters; for example, a smart band may be managed to provide a given level of redundancy for critical data, while not exceeding a specified level of energy usage. This may include using a variety of optimization techniques described throughout this disclosure and the documents incorporated herein by reference.

[1036] In embodiments, such methods and systems for intelligent management of smart bands include an expert system and supporting technology components, services, processes, modules, applications and interfaces, for managing the smart bands (collectively referred to in some cases as a smart band platform 10722), which may include a model-based expert system, a rule-based expert system, an expert system using artificial intelligence (such as a machine learning system, which may include a neural net expert system, a self-organizing map system, a human-supervised machine learning system, a state determination system, a classification system, or other artificial intelligence system), or various hybrids or combinations of any of the above. References to an expert system should be understood to encompass utilization of any one of the foregoing or suitable combinations, except where context indicates otherwise. Intelligent management may be of data collection of various types of data (e.g., vibration data, noise data and other sensor data of the types described throughout this disclosure) for event detection, state detection, and the like. Intelligent management may include managing a plurality of smart bands each directed at supporting an identified application, process or workflow, such as confirming progress toward or alignment with one or more objectives, goals, rules, policies, or guidelines. Intelligent management may also involve managing data collection bands targeted to backing out an unknown variable based on collection of other data (such as based on a model of the behavior of a system that involves the variable), selecting preferred inputs among available inputs (including specifying combinations, fusions, or multiplexing of inputs), and/or specifying an input band among available input bands.

[1037] Data collection bands, or smart bands, may include any number of items such as sensors, input channels, data locations, data streams, data protocols, data extraction techniques, data transformation techniques, data loading techniques, data types, frequency of sampling, placement of sensors, static data points, metadata, fusion of data, multiplexing of data, and the like as described herein. Smart band settings, which may be used interchangeably with smart band and data collection band, may describe the configuration and makeup of the smart band, such as by specifying the parameters that define the smart band. For example, data collection bands, or smart bands, may include one or more frequencies to measure. Frequency data may further include at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope, as well as other signal characteristics described throughout this disclosure. Smart bands may include sensors measuring or data regarding one or more wavelengths, one or more spectra, and/or one or more types of data from various sensors and metadata. Smart bands may include one or more sensors or types of sensors of a wide range of types, such as described throughout this disclosure and the documents incorporated by reference herein. Indeed, the sensors described herein may be used in any of the methods or systems described throughout this disclosure. For example, one sensor may be an accelerometer, such as one that measures voltage per G (“V/G”) of acceleration (e.g., 100 mV/G, 500 mV/G, 1 V/G, 5 V/G, 10 V/G, and the like). In embodiments, the data collection band circuit may alter the makeup of the subset of the plurality of sensors used in a smart band based on optimizing the responsiveness of the sensor, such as for example choosing an accelerometer better suited for measuring acceleration of a low speed mixer versus one better suited for measuring acceleration of a high speed industrial centrifuge. Choosing may be done intelligently, such as for example with a proximity probe and multiple accelerometers disposed on a centrifuge where while at low speed, one accelerometer is used for measuring in the smart band and another is used at high speeds. Accelerometers come in various types, such as piezo-electric crystal, low frequency (e.g., 10 V/G), high speed compressors (10 MV/G), MEMS, and the like. In another example, one sensor may be a proximity probe which can be used for sleeve or tilt-pad bearings (e.g., oil bath), or a velocity probe. In yet another example, one sensor may be a solid-state relay (SSR) that is structured to automatically interface with a routed data collector (such as a mobile or portable data collector) to obtain or deliver data. In another example, a mobile or portable data collector may be routed to alter the makeup of the plurality of available sensors, such as by bringing an appropriate accelerometer to a point of sensing, such as on or near a component of a machine. In still another example, one sensor may be a triax probe (e.g., a 100 MV/G triax probe), that in embodiments is used for portable data collection. In some embodiments, of a triax probe, a vertical element on one axis of the probe may have a high frequency response while the ones mounted horizontally may influence the frequency response of the whole triax. In another example, one sensor may be a temperature sensor and may include a probe with a temperature sensor built inside, such as to obtain a bearing temperature. In still additional examples, sensors may be ultrasonic, microphone, touch, capacitive, vibration, acoustic, pressure, strain gauges, thermographic (e.g., camera), imaging (e.g., camera, laser, IR, structured light), a field detector, an EMF meter to measure an AC electromagnetic field, a gaussmeter, a motion detector, a chemical detector, a gas detector, a CBRNE detector, a vibration transducer, a magnetometer, positional, location-based, a velocity sensor, a displacement sensor, a tachometer, a flow sensor, a level sensor, a proximity sensor, a pH sensor, a hygrometer/moisture sensor, a densitometric sensor, an anemometer, a viscometer, or any analog industrial sensor and/or digital industrial sensor. In a further example, sensors may be directed at detecting or measuring ambient noise, such as a sound sensor or microphone, an ultrasound sensor, an acoustic wave sensor, and an optical vibration sensor (e.g., using a camera to see oscillations that produce noise). In still another example, one sensor may be a motion detector.

[1038] Data collection bands, or smart bands, may be of or may be configured to encompass one or more frequencies, wavelengths, or spectra for particular sensors, for particular groups of sensors, or for combined signals from multiple sensors (such as involving multiplexing or sensor fusion).

[1039] Data collection bands, or smart bands, may be of or may be configured to encompass one or more sensors or sensor data (including groups of sensors and combined signals) from one or more pieces of equipment/components, areas of an installation, disparate but interconnected areas of an installation (e.g., a machine assembly line and a boiler room used to power the line), or locations (e.g., a building in Cambridge and a building in Boston). Smart band settings, configurations, instructions, or specifications (collectively referred to herein using any one of those terms) may include where to place a sensor, how frequently to sample a data point or points, the granularity at which a sample is taken (e.g., a number of sampling points per fraction of a second), which sensor of a set of redundant sensors to sample, an average sampling protocol for redundant sensors, and any other aspect that would affect data acquisition.

[1040] Within the smart band platform 10722, an expert system, which may comprise a neural net, a model-based system, a rule-based system, a machine learning data analysis circuit, and/or a hybrid of any of those, may begin iteration towards convergence on a smart band that is optimized for a particular goal or outcome, such as predicting and managing performance, health, or other characteristics of a piece of equipment, a component, or a system of equipment or components. Based on continuous or periodic analysis of sensor data, as patterns/trends are identified, or outliers appear, or a group of sensor readings begin to change, etc., the expert system may modify its data collection bands intelligently. This may occur by triggering a rule that reflects a model or understanding of system behavior (e.g., recognizing a shift in operating mode that calls for different sensors as velocity of a shaft increases) or it may occur under control of a neural net (either in combination with a rule-based approach or on its own), where inputs are provided such that the neural net over time learns to select appropriate collection modes based on feedback as to successful outcomes (e.g., successful classification of the state of a system, successful prediction, successful operation relative to a metric, or the like). For example, when a new pressure reactor is installed in a chemical processing facility, data from the current data collection band may not accurately predict the state or metric of operation of the system, thus, the machine learning data analysis circuit may begin to iterate to determine if a new data collection band is better at predicting a state. Based on offset system data, such as from a library or other data structure, certain sensors, frequency bands or other smart band members may be used in the smart band initially and data may be collected to assess performance. As the neural net iterates, other sensors/frequency bands may be accessed to determine their relative weight in identifying performance metrics. Over time, a new frequency band may be identified (or a new collection of sensors, a new set of configurations for sensors, or the like) as a better gauge of performance in the system and the expert system may modify its data collection band based on this iteration. For example, perhaps a slightly different or older associated turbine agitator in a chemical reaction facility dampens one or more vibration frequencies while a different frequency is of higher amplitude and present during optimal performance than what was seen in the offset system. In this example, the smart band may be altered from what was suggested by the corresponding offset system to capture the higher amplitude frequency that is present in the current system.

[1041] The expert system, in embodiments involving a neural net or other machine learning system, may be seeded and may iterate, such as towards convergence on a smart band, based on feedback and operation parameters, such as described herein. Certain feedback may include utilization measures, efficiency measures (e.g., power or energy utilization, use of storage, use of bandwidth, use of input/output use of perishable materials, use of fuel, and/or financial efficiency), measures of success in prediction or anticipation of states (e.g., avoidance and mitigation of faults), productivity measures (e.g., workflow), yield measures, and profit measures. Certain parameters may include: storage parameters (e.g., data storage, fuel storage, storage of inventory and the like); network parameters (e.g., network bandwidth, input/output speeds, network utilization, network cost, network speed, network availability and the like); transmission parameters (e.g., quality of transmission of data, speed of transmission of data, error rates in transmission, cost of transmission and the like); security parameters (e.g., number and/or type of exposure events; vulnerability to attack, data loss, data breach, access parameters, and the like); location and positioning parameters (e.g., location of data collectors, location of workers, location of machines and equipment, location of inventory units, location of parts and materials, location of network access points, location of ingress and egress points, location of landing positions, location of sensor sets, location of network infrastructure, location of power sources and the like); input selection parameters, data combination parameters (e.g., for multiplexing, extraction, transformation, loading, and the like); power parameters; states (e.g., operating modes, availability states, environmental states, fault modes, maintenance modes, anticipated states); events; and equipment specifications. With respect to states, operating modes may include mobility modes (direction, speed, acceleration, and the like), type of mobility modes (e.g., rolling, flying, sliding, levitation, hovering, floating, and the like), performance modes (e.g., gears, rotational speeds, heat levels, assembly line speeds, voltage levels, frequency levels, and the like), output modes, fuel conversion modes, resource consumption modes, and financial performance modes (e.g., yield, profitability, and the like). Availability states may refer to anticipating conditions that could cause machine to go offline or require backup. Environmental states may refer to ambient temperature, ambient humidity/moisture, ambient pressure, ambient wind/fluid flow, presence of pollution or contaminants, presence of interfering elements (e.g., electrical noise, vibration), power availability, and power quality. Anticipated states may include: achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/failure rate, a financial efficiency/profit goal, a power efficiency/resource utilization; an avoidance of a fault condition (e.g., overheating, slow performance, excessive speed, excessive motion, excessive vibration/oscillation, excessive acceleration, expansion/contraction, electrical failure, running out of stored power/fuel, overpressure, excessive radiation/melt down, fire, freezing, failure of fluid flow (e.g., stuck valves, frozen fluids); mechanical failures (e.g., broken component, worn component, faulty coupling, misalignment, asymmetries/deflection, damaged component (e.g., deflection, strain, stress, cracking], imbalances, collisions, jammed elements, and lost or slipping chain or belt); avoidance of a dangerous condition or catastrophic failure; and availability (online status).

[1042] The expert system may comprise or be seeded with a model that predicts an outcome or state given a set of data (which may comprise inputs from sensors, such as via a data collector, as well as other data, such as from system components, from external systems and from external data sources). For example, the model may be an operating model for an industrial environment, machine, or workflow. In another example, the model may be for anticipating states, for predicting fault and optimizing maintenance, for self-organizing storage (e.g., on devices, in data pools and/or in the cloud), for optimizing data transport (such as for optimizing network coding, network-condition-sensitive routing, and the like), for optimizing data marketplaces, and the like.

[1043] The iteration of the expert system may result in any number of downstream actions based on analysis of data from the smart band. In an embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given a desired goal, such as a specified/threshold output production rate, specified/threshold generation rate, an operational efficiency/failure rate, a financial efficiency/profit goal, a power efficiency/resource utilization, an avoidance of a fault condition, an avoidance of a dangerous condition or catastrophic failure, and the like. In embodiments, the adjustments may be based on determining context of an industrial system, such as understanding a type of equipment, its purpose, its typical operating modes, the functional specifications for the equipment, the relationship of the equipment to other features of the environment (including any other systems that provide input to or take input from the equipment), the presence and role of operators (including humans and automated control systems), and ambient or environmental conditions. For example, in order to achieve a profit goal, a pipeline in a refinery may need to operate for a certain amount of time a day and/or at a certain flow rate. The expert system may be seeded with a model for operation of the pipeline in a manner that results in a specified profit goal, such as indicating a given flow rate of material through the pipeline based on the current market sale price for the material and the cost of getting the material into the pipeline. As it acquires data and iterates, the model will predict whether the profit goal will be achieved given the current data. Based on the results of the iteration of the expert system, a recommendation may be made (or a control instruction may be automatically provided) to operate the pipeline at a higher flow rate, to keep it operational for longer or the like. Further, as the system iterates, one or more additional sensors may be sampled in the model to determine if their addition to the smart band would improve predicting a state. In another embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net or other model given a constraint of operation (e.g., meeting a required endpoint (e.g., delivery date, amount, cost, coordination with another system), operating with a limited resource (e.g., power, fuel, battery), storage (e.g., data storage), bandwidth (e.g., local network, p2p, WAN, internet bandwidth, availability, or input/output capacity), authorization (e.g., role-based)), a warranty limitation, a manufacturer's guideline, a maintenance guideline). For example, a constraint of operating a boiler in a refinery is that the aeration of the boiler feedwater needs to be reduced in the cycle; therefore, the boiler must coordinate with the deaerator. In this example, the expert system is seeded with a model for operation of the boiler in coordination with the de-aerator that results in a specified overall performance. As sensor data from the system is acquired, the expert system may determine that an aspect of one or both of the boiler and aerator must be changed to continue to achieve the specific overall performance. In a further embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given an identified choke point. In still another embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given an off-nominal operation. For example, a reciprocating compressor in a refinery that delivers gases at high pressure may be measured as having an off-nominal operation by sensors that feed their data into an expert system (optionally including a neural net or other machine learning system). As the expert system iterates and receives the off-nominal data, it may predict that the refinery will not achieve a specified goal and will recommend an action, such as taking the reciprocating compressor offline for maintenance. In another embodiment, the expert system may determine that the system should collect more/fewer data points from one or more sensors. For example, an anchor agitator in a pharmaceutical processing plant may be programmed to agitate the contents of a tank until a certain level of viscosity (e.g., as measured in centipoise) is obtained. As the expert system collects data throughout the run indicating an increase in viscosity, the expert system may recommend collecting additional data points to confirm a predicted state in the face of the increased strain on the plant systems from the viscosity. In yet another embodiment, the expert system may determine that the system should change a data storage technique. In still another example, the expert system may determine that the system should change a data presentation mode or manner. In a further embodiment, the expert system may determine that the system should apply one or more filters (low pass, high pass, band pass, etc.) to collected data. In yet a further embodiment, the expert system may determine that the system should collect data from a new smart band/new set of sensors and/or begin measuring a new aspect that the neural net identified itself. For example, various measurements may be made of paddle-type agitator mixers operating in a pharmaceutical plant, such as mixing times, temperature, homogeneous substrate distribution, heat exchange with internal structures and the tank wall or oxygen transfer rate, mechanical stress, forces and torques on agitator vessels and internal structures, and the like. Various sensor data streams may be included in a smart band monitoring these various aspects of the paddle-type agitator mixer, such as a flow meter, a thermometer, and others. As the expert system iterates, perhaps having been seeded with minimal data from during the agitator's run, a new aspect of the operation may become apparent, such as the impact of pH on the state of the run. Thus, a new smart band will be identified by the expert system that includes sensor data from a pH meter. In yet still a further embodiment, the expert system may determine that the system should discontinue collection of data from a smart band, one or more sensors, or the like. In another embodiment, the expert system may determine that the system should initiate data collection from a new smart band, such as a new smart band identified by the neural net itself. In yet another embodiment, the expert system may determine that the system should adjust the weights/biases of a model used by the expert system. In still another embodiment, the expert system may determine that the system should remove/re-task under-utilized equipment. For example, a plurality of agitators working with a pump blasting liquid in a pharmaceutical processing plant may be monitored during operation of the plant by the expert system. Through iteration of the expert system seeded with data from a run of the plant with the agitators, the expert system may predict that a state will be achieved even if one or more agitators are taken out of service.

[1044] In embodiments, a monitoring system for data collection in an industrial environment may include a plurality of input sensors, such as any of those described herein, communicatively coupled to a data collector having a controller. The monitoring system may include a data collection band circuit structured to determine at least one subset of the plurality of sensors from which to process output data. The monitoring system may also include a machine learning data analysis circuit structured to receive output data from the at least one subset of the plurality of sensors and learn received output data patterns indicative of a state. In some embodiments, the data collection band circuit may alter the at least one subset of the plurality of sensors, or an aspect thereof, based on one or more of the learned received output data patterns and the state. In certain embodiments, the machine learning data analysis circuit is seeded with a model that enables it to learn data patterns. The model may be a physical model, an operational model, a system model, and the like. In other embodiments, the machine learning data analysis circuit is structured for deep learning wherein input data is fed to the circuit with no or minimal seeding and the machine learning data analysis circuit learns based on output feedback. For example, a static mixer in a chemical processing plant producing polymers may be used to facilitate the polymerization reaction. The static mixer may employ turbulent or laminar flow in its operation. Minimal data, such as heat transfer, velocity of flow out of the mixer, Reynolds number or pressure drop, acquired during the operation of the static mixer may be fed into the expert system which may iterate towards a prediction based on initial feedback (e.g., viscosity of the polymer, color of the polymer, reactivity of the polymer).

[1045] There may be a balance of multiple goals/guidelines in the management of smart bands by the expert system. For example, a repair and maintenance organization (RMO) may have operating parameters designed for maintenance of a storage tank in a refinery, while the owner of the refinery may have particular operating parameters for the storage tank that are designed for meeting a production goal. These goals, in this example relating to a maintenance goal or a production output, may be tracked by a different data collection bands. For example, maintenance of a storage tank may be tracked by sensors including a vibration transducer and a strain gauge, while the production goal of a storage tank may be tracked by sensors including a temperature sensor and a flow meter. The expert system may (optionally using a neural net, machine learning system, deep learning system, or the like, which may occur under supervision by one or more supervisors (human or automated)) intelligently manage bands aligned with different goals and assign weights, parameter modifications, or recommendations based on a factor, such as a bias towards one goal or a compromise to allow better alignment with all goals being tracked, for example. Compromises among the goals delivered to the expert system may be based on one or more hierarchies or rules (relating to the authority, role, criticality, or the like) of the applicable goals. In embodiments, compromises among goals may be optimized using machine learning, such as a neural net, deep learning system, or other artificial intelligence system as described throughout this disclosure. In one illustrative example, in a chemical processing plant where a gas-powered agitator is operating, the expert system may manage multiple smart bands, such as one directed to detecting the operational status of the gas-powered agitator, one directed at identifying a probability of hitting a production goal, and one directed at determining if the operation of the gas-powered agitator is meeting a fuel efficiency goal. Each of these smart bands may be populated with different sensors or data from different sensors (e.g., a vibration transducer to indicate operational status, a flow meter to indicate production goal, and a fuel gauge to indicate a fuel efficiency) whose output data are indicative of an aspect of the particular goal. Where a single sensor or a set of sensors is helpful for more than one goal, overlapping smart bands (having some sensors in common and other sensors not in common) may take input from that sensor or set of sensors, as managed by the smart band platform 10722. If there are constraints on data collection (such as due to power limitations, storage limitations, bandwidth limitations, input/output processing capabilities, or the like), a rule may indicate that one goal (e.g., a fuel utilization goal or a pollution reduction goal that is mandated by law or regulation) takes precedence, such that the data collection for the smart bands associated with that goal are maintained as others are paused or shut down. Management of prioritization of goals may be hierarchical or may occur by machine learning. The expert system may be seeded with models, or may not be seeded at all, in iterating towards a predicted state (i.e., meeting the goal) given the current data it has acquired. In this example, during operation of the gas-powered agitator, the plant owner may decide to bias the system towards fuel efficiency. All of the bands may still be monitored, but as the expert system iterates and predicts that the system will not meet or is not meeting a particular goal, and then offers recommended changes directed at increasing the chance of meeting the goal, the plant owner may structure the system with a bias towards fuel efficiency so that the recommended changes to parameters affecting fuel efficiency are made in favor of making other recommended changes.

[1046] In embodiments, the expert system may continue iterating in a deep-learning fashion to arrive at a single smart band, after being seeded with more than one smart band, that optimizes meeting more than one goal. For example, there may be multiple goals tracked for a thermic heating system in a chemical processing or a food processing plant, such as thermal efficiency and economic efficiency. Thermal efficiency for the thermic heating system may be expressed by comparing BTUs put in to the system, which can be obtained by knowing the amount of and quality of the fuel being used, and the BTUs out of the system, which is calculated using the flow out of the system and the temperature differential of materials in and out of the system. Economic efficiency of the thermic heating system may be expressed as the ratio between costs to run the system (including fuel, labor, materials, and services) and energy output from the system for a period of time. Data used to track thermal efficiency may include data from a flow meter, quality data point(s), and a thermometer, and data used to track economic efficiency may be an energy output from the system (e.g., kWh) and costs data. These data may be used in smart bands by the expert system to predict states, however, the expert system may iterate toward a smart band that is optimized to predict states related to both thermal and economic efficiency. The new smart band may include data used previously in the individual smart bands but may also use new data from different sensors or data sources. In embodiments, the expert system may be seeded with a plurality of smart bands and iterate to predict various states, but may also iterate towards reducing the number of smart bands needed to predict the same set of states.

[1047] Iteration of the expert system may be governed by rules, in some embodiments. For example, the expert system may be structured to collect data for seeding at a pre-determined frequency. The expert system may be structured to iterate at least a number of times, such as when a new component/equipment/fuel source is added, when a sensor goes off-line, or as standard practice. For example, when a sensor measuring the rotation of a stirrer in a food processing line goes off-line and the expert system begins acquiring data from a new sensor measuring the same data points, the expert system may be structured to iterate for a number of times before the state is utilized in or allowed to affect any downstream actions. The expert system may be structured to train off-line or train in situ/online. The expert system may be structured to include static and/or manually input data in its smart bands. For example, an expert system managing smart bands associated with a mixer in a food processing plant may be structured to iterate towards predicting a duration of mixing before the food being processed achieves a particular viscosity, wherein the smart band includes data regarding the speed of the mixer, temperature of its contents, viscometric measurements and the required endpoint for viscosity and temperature of the food. The expert system may be structured to include a minimum/maximum number of variables.

[1048] In embodiments, the expert system may be overruled. In embodiments, the expert system may revert to prior band settings, such as in the event the expert system fails, such as if a neural network fails in a neural net expert system, if uncertainty is too high in a model-based system, if the system is unable to resolve conflicting rules in rule-based system, or the system cannot converge on a solution in any of the foregoing. For example, sensor data on an irrigation system used by the expert system in a smart band may indicate a massive leak in the field, but visual inspection, such as by a drone, indicates no such leak. In this event, the expert system will revert to an original smart band for seeding the expert system. In another example, one or more point sensors on an industrial pressure cooker indicates imminent failure in a seal, but the data collection band that the expert system converged to with a weighting towards a performance metric did not identify the failure. In this event, the smart band will revert to an original setting or a version of the smart band that would have also identified the imminent failure of the pressure cooker seal. In embodiments, the expert system may change smart band settings in the event that a new component is added that makes the system closer to a different offset system. For example, a vacuum distillation unit is added to an oil & gas refinery to distill naphthalene, but the current smart band settings for the expert system are derived from a refinery that distills kerosene. In this example, a data structure with smart band settings for various offset systems may be searched for a system that is more closely matched to the current system. When a new offset system is identified as more closely matched, such as one that also distill naphthalene, the new smart band settings (e.g., which sensors to use, where to place them, how frequently to sample, what static data points are needed, etc. as described herein) are used to seed the expert system to iterate towards predicting a state for the system. In embodiments, the expert system may change smart band settings in the event that a new set of offset data is available from a third-party library. For example, a pharmaceutical processing plant may have optimized a catalytic reactor to operate in a highly efficient way and deposited the smart band settings in a data structure. The data structure may be continuously scanned for new smart bands that better aid in monitoring catalytic reactions and thus, result in optimizing the operation of the reactor.

[1049] In embodiments, the expert system may be used to uncover unknown variables. For example, the expert system may iterate to identify a missing variable to be used for further iterations, such as further neural net iterations. For example, an under-utilized tank in a legacy condensate/make-up water system of a power station may have an unknown capacity because it is inaccessible and no documentation exists on the tank. Various aspects of the tank may be measured by a swarm of sensors to arrive at an estimated volume (e.g., flow into a downstream space, duration of a dye traced solution to work through the system), then that volume can be fed into the neural net as a new variable in the smart band.

[1050] In embodiments, the location of expert system node locations may be on a machine, on a data collector (or a group of them), in a network infrastructure (enterprise or other), or in the cloud. In embodiments, there may be distributed neurons across nodes (e.g., machine, data collector, network, cloud).

[1051] Referring to FIG. 140, in an aspect, a monitoring system 10700 for data collection in an industrial environment, comprising a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state. The data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state. The state may correspond to an outcome relating to a machine in the environment, an anticipated outcome relating to a machine in the environment, an outcome relating to a process in the environment, an anticipated outcome relating to a process in the environment, and the like. The collection parameter may be a bandwidth parameter, may be used to govern the multiplexing of a plurality of the input sensors, may be a timing parameter, may relate to a frequency range, may relate to the granularity of collection of sensor data, is a storage parameter for the collected data. The machine learning data analysis circuit may be structured to learn received output data patterns 10718 by being seeded with a model 10720, which may be a physical model, an operational model, or a system model. The machine learning data analysis circuit may be structured to learn received output data patterns 10718 based on the state. The data collection band circuit may alter the subset of the plurality of sensors when the learned received output data pattern does not reliably predict the state, which may include discontinuing collection of data from the at least one subset.

[1052] The monitoring system 10700 may keep or modify operational parameters of an item of equipment in the environment based on the determined state. The controller 10706 may adjust the weighting of the machine learning data analysis circuit 10712 based on the learned received output data patterns 10718 or the state. The controller 10706 may collect more/fewer data points from one or more members of the at least one subset of plurality of sensors 10702 based on the learned received output data patterns 10718 or the state. The controller 10706 may change a data storage technique for the output data 10710 based on the learned received output data patterns 10718 or the state. The controller 10706 may change a data presentation mode or manner based on the learned received output data patterns 10718 or the state. The controller 10706 may apply one or more filters to the output data 10710. The controller 10706 may identify a new data collection band circuit 10708 based on one or more of the learned received output data patterns 10718 and the state. The controller 10706 may adjust the weights/biases of the machine learning data analysis circuit 10712, such as in response to the learned received output data patterns 10718, in response to the accuracy of the prediction of an anticipated state by the machine learning data analysis circuit, in response to the accuracy of a classification of a state by the machine learning data analysis circuit, and the like. The monitoring device 10700 may remove or re-task under-utilized equipment based on one or more of the learned received output data patterns 10718 and the state. The machine learning data analysis circuit 10712 may include a neural network expert system. At least one subset of the plurality of sensors measures vibration and noise data. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of progress/alignment with one or more goals/guidelines, wherein progress/alignment of each goal/guideline may be determined by a different subset of the plurality of sensors. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of an unknown variable. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of a preferred input among available inputs. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of a preferred input data collection band among available input data collection bands. The machine learning data analysis circuit 10712 may be disposed in part on a machine, on one or more data collectors, in network infrastructure, in the cloud, or any combination thereof.

[1053] In embodiments, a monitoring device for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a controller 10706, the controller 10706 including a data collection band circuit 10708 structured to determine at least one subset of the plurality of sensors 10702 from which to process output data 10710; and a machine learning data analysis circuit 10712 structured to receive output data from the at least one subset of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters an aspect of the at least one subset of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state. The aspect that the data collection band circuit 10708 alters is a number or a frequency of data points collected from one or more members of the at least one subset of plurality of sensors 10702. The aspect that the data collection band circuit 10708 alters is a bandwidth parameter, a timing parameter, a frequency range, a granularity of collection of sensor data, a storage parameter for the collected data, and the like.

[1054] In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the data collection band circuit 10708 alters the at least one of the plurality of sensors 10702 when the learned received output data pattern 10718 does not reliably predict the state.

[1055] In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the data collector 10704 collects more or fewer data points from the at least one of the plurality of sensors 10702 based on the learned received output data patterns 10718 or the state.

[1056] In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data 10710 patterns indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 changes a data storage technique for the output data 10710 based on the learned received output data patterns 10718 or the state.

[1057] In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 changes a data presentation mode or manner based on the learned received output data patterns 10718 or the state.

[1058] In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 identifies a new data collection band circuit 10708 based on one or more of the learned received output data patterns 10718 and the state.

[1059] In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 adjusts the weights/biases of the machine learning data analysis circuit 10712. The adjustment may be in response to the learned received output data patterns, in response to the accuracy of the prediction of an anticipated state by the machine learning data analysis circuit, in response to the accuracy of a classification of a state by the machine learning data analysis circuit, and the like.

[1060] In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712. This machine learning data analysis circuit is structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the machine learning data analysis circuit 10712 is structured to learn received output data patterns 10718 indicative of progress or alignment with one or more goals or guidelines.