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… of diagnosing machine components using analog sensor data and neural network — Strong Force Iot Portfolio 2016, Llc (US11755878B2)

Strong Force Iot Portfolio 2016, Llc · Google Patents

Google Patents · Patents · License: Open Access

patent, google patents, intellectual property, US11755878B2, Strong Force Iot Portfolio 2016, Llc, Charles Howard Cella, en, 2023

ABSTRACT

Abstract

Systems and methods for data collection in an industrial environment are disclosed. A system can include a plurality of analog sensors, wherein each of the plurality of analog sensors is operationally coupled to a respective data collection point of a machine component, and generates a respective stream of detection values. A data acquisition and analysis circuit can receive the respective stream of detection values and analyze the respective stream of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition based on an analysis of the respective stream of detection values, wherein the expert system analysis circuit utilizes a neural network including one of a probabilistic, a time delay, and a convolutional neural network.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of, and is a continuation of, U.S. Non-Provisional patent application Ser. No. 16/143,360, filed Sep. 26, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH A SELF-ORGANIZING ADAPTIVE SENSOR SWARM FOR INDUSTRIAL PROCESSES (STRF-0019-U01).

U.S. Ser. No. 16/143,360 (STRF-0019-U01) 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 (STRF-0001-U22).

U.S. Ser. No. 15/973,406 (STRF-0001-U22) 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 (STRF-0001-WO), which claims priority to: U.S. Provisional Patent Application Ser. No. 62/333,589, filed May 9, 2016, entitled STRONG FORCE INDUSTRIAL IOT MATRIX (STRF-0001-P01); 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 (STRF-0001-P02); U.S. Provisional Patent Application Ser. No. 62/412,843, filed Oct. 26, 2016, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P03); 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 (STRF-0001-P04).

U.S. Ser. No. 15/973,406 (STRF-0001-U22) also 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 (STRF-0001-P05); U.S. Provisional Patent Application Ser. No. 62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P06); 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 (STRF-0001-P07).

U.S. Ser. No. 16/143,360 (STRF-0019-U01) 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 (STRF-0011-WO).

International Application Number PCT/US18/45036 (STRF-0011-WO) claims the benefit of, and 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 (STRF-0001-U22).

International Application Number PCT/US18/45036 (STRF-0011-WO) 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 (STRF-0001-P05); 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 (STRF-0001-P08); U.S. Provisional Patent Application Ser. No. 62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P06); 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 (STRF-0001-P07).

U.S. Ser. No. 16/143,360 (STRF-0019-U01) claims priority to U.S. Provisional Patent Application Ser. No. 62/583,487, filed Nov. 8, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P07).

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

BACKGROUND

1. Field

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.

2. Description of the Related Art

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.

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 âsmartâ 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.

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 an 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.

SUMMARY

The present disclosure describes system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a plurality of analog sensors, wherein each of the plurality of analog sensors is operationally coupled to a respective data collection point of a machine component in an industrial environment and generates a respective stream of detection values relating to the respective data collection point of the machine component, wherein at least one of the plurality of analog sensors monitors a rotating machine component, and a data acquisition and analysis circuit for receiving the respective stream of detection values and structured to analyze the respective stream of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition for the rotating machine component based on an analysis of the respective stream of detection values, wherein the expert system analysis circuit utilizes a neural network including one of a probabilistic, a time delay, and a convolutional neural network.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the neural network includes a probabilistic neural network that determines the occurrence of the anomalous condition based on pattern recognition.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the neural network includes a time delay neural network that determines the occurrence of the anomalous condition based on pattern recognition.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the time delay neural network is trained with machine learning.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an analyzed stream of detection values that represents sound.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the neural network is a convolutional neural network that determines the occurrence of the anomalous condition based on pattern recognition.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an analyzed stream of detection values that includes image data.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an analyzed stream of detection values data includes video data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein one of the plurality of analog sensors includes a tri-axial sensor for monitoring different positions of the rotating machine component.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system analysis circuit is configured to analyze a respective stream of detection values from a first and a second of the plurality of analog sensors to determine a relative phase at one or more times, and wherein the expert system analysis circuit is further configured to determine a failure state in response to the determined relative phase.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system analysis circuit further controls a plurality of data collection bands for determining collection schedules for different groupings of the plurality of analog sensors.

The present disclosure describes a computer-implemented method for data collection in an industrial environment, the method according to one disclosed non-limiting embodiment of the present disclosure can include collecting streams of detection values relating to a plurality of machine components by a plurality of analog sensors, wherein each of the plurality of analog sensor is operationally coupled to a respective data collection point of a machine component in an industrial environment and generates a respective stream of detection values relating to the respective data collection point of the machine component, wherein at least one of the plurality of analog sensors monitors a rotating machine component, and analyzing the streams of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition for the rotating machine component based on an analysis of the streams of detection values, wherein the expert system analysis circuit utilizes a neural network including one of a probabilistic, a time delay, and a convolutional neural network.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining a failure state for the rotating machine component based on the analysis and providing the failure state to a data storage.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include analyzing a first stream of detection values corresponding to a first analog sensor and a second stream of detection values corresponding to a second analog sensor for a relative phase determination and detecting the failure state for the rotating machine component in response to the relative phase determination.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating the expert system analysis circuit to control data collection bands of a plurality of input channels.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the neural network includes a time delay neural network and an analyzed stream of detection values represents sound.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the neural network includes a convolutional neural network that determines the occurrence of the anomalous condition based on pattern recognition of an analyzed stream of detection values which represents image data.

The present disclosure describes a system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a plurality of analog sensors, wherein each analog sensor is operationally coupled to a respective data collection point of a machine component in an industrial environment and generates a respective stream of detection values relating to the respective data collection point of the machine component, wherein at least one of the analog sensors monitors a rotating machine component, a means for receiving the respective stream of detection values and structured to analyze the respective stream of detection values using an expert system analysis circuit, and a means for determining an occurrence of an anomalous condition for the rotating machine component based on an analysis of the respective stream of detection values, wherein the means utilize a neural network including one of a probabilistic, a time delay, and a convolutional neural network.

A furt

CROSS-REFERENCE TO RELATED APPLICATIONS

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

BACKGROUND

1. Field

2. Description of the Related Art

SUMMARY

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the time delay neural network is trained with machine learning.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an analyzed stream of detection values that represents sound.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an analyzed stream of detection values that includes image data.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an analyzed stream of detection values data includes video data.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include a means for analyzing a respective stream of detection values from a first and a second of the plurality of analog sensors to determine a relative phase at one or more times, and further including a means for determining a failure state in response to the determined relative phase.

In an aspect, methods, systems, and apparatus for detection in an industrial internet of things data collection environment with a self-organizing adaptive sensor swarm for industrial processes include a plurality of data collectors communicatively coupled to a plurality of input channels, wherein each of the plurality of data collectors is structured to collect detection values as collected data, an expert system circuit structured to self-organize one or more detection packages and an associated subset of the plurality of data collectors using a swarm optimization algorithm, a data acquisition circuit structured to interpret the collected data, a data analysis circuit structured to analyze the collected data, and a cognitive input selection facility for optimization of an input selection configuration for a collector route of the plurality of data collectors. The input selection configuration may be based on a learning feedback from a learning feedback facility which may be a remote learning feedback facility associated with a data collection marketplace, and the learning feedback is derived from user feedback metrics. The plurality of data collectors may be a self-organized swarm of data collectors, wherein the self-organized swarm of data collectors organizes among themselves to optimize data collection based at least in part on a received data marketplace indicator. The self-organized swarm of data collectors may coordinate with one another to optimize data collection based at least in part on the received data marketplace indicator or on optimizing sensed parameters from the collected data over time. The user feedback metrics may be based on market usage of the collected data over time. The cognitive input selection facility may derive input selection from a self-organizing data marketplace for industrial Internet-of-things data that comprises at least in part data collected by the system. The optimization of the input selection configuration may modify a hierarchical template for data collection. The cognitive input selection facility may anticipate state information from machine learning and pattern recognition to optimize the input selection configuration. The cognitive input selection facility may iterate based on feedback to a machine learning facility regarding measures of success. The measures of success may include at least one of utilization measures, efficiency measures, measures of success in prediction or anticipation of states, productivity measures, yield measures, or profit measures.

In an aspect, a method for detection in an industrial internet of things data collection environment with a self-organizing adaptive sensor swarm for industrial processes includes collecting data from a plurality of input channels by a plurality of data collectors communicatively coupled to the plurality of input channels, wherein each of the plurality of data collectors is structured to collect detection values as collected data, self-organizing, by an expert system circuit, one or more detection packages and an associated subset of the plurality of data collectors using a swarm optimization algorithm, interpreting the collected data by a data acquisition circuit, analyzing the collected data by a data analysis circuit, and optimizing an input selection configuration by a cognitive input selection facility for a collector route of the plurality of data collectors. The plurality of data collectors may be a self-organized swarm of data collectors, wherein the self-organized swarm of data collectors organizes among themselves to optimize data collection based at least in part on a received data marketplace indicator. The self-organized swarm of data collectors may coordinate with one another to optimize data collection based at least in part on the received data marketplace indicator or on optimizing sensed parameters from the collected data over time.

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 âedgeâ 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.

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.

Methods and systems are disclosed herein for training artificial intelligence (âAIâ) 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.

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.

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.

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.

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 (âCPLDâ) 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.

In embodiments, the local data collection system includes a phase-lock loop band-pass tracking filter configured to obtain slow-speed revolutions per minute (âRPMsâ) 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.

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.

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.

In embodiments, the local data collection system includes a graphical user interface (âGUIâ) 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 through FIG. 5 are diagrammatic views that each depicts portions of an overall view of an industrial Internet of Things (IoT) data collection, monitoring and control system in accordance with the present disclosure.

FIG. 6 is a diagrammatic view of a platform including a local data collection system disposed in an industrial environment 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 in accordance with the present disclosure.

FIG. 7 is a diagrammatic view that depicts elements of an industrial data collection system for collecting analog sensor data in an industrial environment in accordance with the present disclosure.

FIG. 8 is a diagrammatic view of a rotating or oscillating machine having a data acquisition module that is configured to collect waveform data in accordance with the present disclosure.

FIG. 9 is a diagrammatic view of an exemplary tri-axial sensor mounted

CLAIMS

Claims ( 23 )

What is claimed is:

1. A system comprising:

a plurality of sensors, wherein each of the plurality of sensors is operationally coupled to a respective data collection point of a machine component in an industrial environment and generates a respective stream of detection values relating to the respective data collection point of the machine component, wherein at least one of the plurality of sensors monitors a rotating machine component, and wherein the respective stream of detection values is digitally sampled and filtered waveform data from a respective one of the plurality of sensors; and

a data acquisition and analysis circuit for receiving the respective stream of detection values and structured to analyze the respective stream of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition for the rotating machine component based on an analysis of the respective stream of detection values, wherein the expert system analysis circuit utilizes a neural network including at least one of a probabilistic, a time delay, or a convolutional neural network,

wherein the expert system analysis circuit controls a plurality of data collection bands for determining collection schedules of different groupings of the plurality of sensors, and

wherein the data collection bands each include one or more frequencies to be measured by a respective grouping of the plurality of sensors.

2. The system of claim 1 , wherein the neural network comprises a probabilistic neural network that determines the occurrence of the anomalous condition based on pattern recognition.

3. The system of claim 1 , wherein the neural network comprises a time delay neural network that determines the occurrence of the anomalous condition based on pattern recognition.

4. The system of claim 3 , wherein the time delay neural network is trained with machine learning.

5. The system of claim 1 , further comprising an analyzed stream of detection values that represents sound.

6. The system of claim 1 , wherein the neural network is a convolutional neural network that determines the occurrence of the anomalous condition based on pattern recognition.

7. The system of claim 1 , further comprising an analyzed stream of detection values that comprises image data.

8. The system of claim 1 , further comprising an analyzed stream of detection values data comprises video data.

9. The system of claim 1 , wherein one of the plurality of sensors comprises a tri-axial sensor structured to monitor three orthogonal directions of the rotating machine component.

10. The system of claim 1 , wherein the expert system analysis circuit is configured to analyze respective streams of detection values from a first and a second of the plurality of sensors to determine a relative phase at one or more times, and wherein the expert system analysis circuit is further configured to determine a failure state in response to the determined relative phase.

11. The system of claim 1 , wherein the respective stream of detection values includes at least one of an interpolated waveform or a decimated waveform.

12. The system of claim 1 , wherein the one or more frequencies includes at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, or an overall waveform derived from a vibration envelope.

13. A computer-implemented method for data collection in an industrial environment, the method comprising:

collecting streams of detection values relating to a plurality of machine components by a plurality of sensors, wherein each of the plurality of sensors is operationally coupled to a respective data collection point of a machine component in the industrial environment and generates a respective stream of detection values relating to the respective data collection point of the machine component, wherein at least one of the plurality of sensors monitors a rotating machine component, and wherein the detection values of at least one of the respective streams of detection values are digitally sampled and filtered waveform data from a respective one of the plurality of sensors;

analyzing the streams of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition for the rotating machine component based on an analysis of the streams of detection values, wherein the expert system analysis circuit utilizes a neural network including at least one of: a probabilistic, a time delay, or a convolutional neural network; and

controlling a plurality of data collection bands for determining collection schedules of different groupings of the plurality of sensors, wherein the data collection bands each include one or more frequencies to be measured by a respective grouping of the plurality of sensors.

14. The method of claim 13 , further comprising determining a failure state for the rotating machine component based on the analysis and providing the failure state to a data storage.

15. The method of claim 14 , further comprising analyzing a first stream of detection values corresponding to a first sensor and a second stream of detection values corresponding to a second sensor for a relative phase determination and detecting the failure state for the rotating machine component in response to the relative phase determination.

16. The method of claim 13 , further comprising operating the expert system analysis circuit to control data collection bands of a plurality of input channels.

17. The method of claim 13 , wherein the neural network comprises a probabilistic neural network that determines the occurrence of the anomalous condition based on pattern recognition.

18. The method of claim 13 , wherein the neural network comprises a time delay neural network and an analyzed stream of detection values represents sound.

19. The method of claim 13 , wherein the neural network comprises a convolutional neural network that determines the occurrence of the anomalous condition based on pattern recognition of an analyzed stream of detection values which represents image data.

20. The method of claim 13 , wherein the respective stream of detection values includes at least one of: an interpolated waveform or a decimated waveform.

21. A system comprising:

a plurality of sensors, wherein each sensor is operationally coupled to a respective data collection point of a machine component in an industrial environment and generates a respective stream of detection values relating to the respective data collection point of the machine component, wherein at least one of the sensors monitors a rotating machine component, and wherein the respective stream of detection values is digitally sampled and filtered waveform data from a respective one of the plurality of sensors,

wherein the respective stream of detection values is digitally sampled at an effective sampling rate that is higher at frequency bands proximal to an operating speed of the rotating machine component;

a means for receiving the respective stream of detection values and structured to analyze the respective stream of detection values using an expert system analysis circuit; and

a means for determining an occurrence of an anomalous condition for the rotating machine component based on an analysis of the respective stream of detection values, wherein the means utilize a neural network including at least one of: a probabilistic, a time delay, or a convolutional neural network.

22. The system of claim 21 , further comprising a means for analyzing respective streams of detection values from a first and a second of the plurality of sensors to determine a relative phase at one or more times, and further comprising a means for determining a failure state in response to the determined relative phase.

23. The system of claim 21 , wherein the effective sampling rate is realized by means for interpolating and decimating the respective stream of detection values.

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