Systems and methods for data collection including pattern recognition

• US 11,131,989 B2
• Filed: 06/29/2019
• Issued: 09/28/2021
• Est. Priority Date: 08/02/2017
• Status: Active Grant

• Current Assignee: Strong Force IoT Portfolio 2016, LLC (Strong Force Intellectual Capital, LLC)
• Original Assignee: Strong Force IoT Portfolio 2016, LLC (Strong Force Intellectual Capital, LLC)
• Inventors: Cella, Charles Howard, Duffy, Jr., Gerald William, McGuckin, Jeffrey P., Desai, Mehul
• Primary Examiner(s): Kuan, John C
• Application Number:
  US16/458,051
• Publication Number:
  US 20190324433A1
• Time in Patent Office: 822 Days
• Field of Search: None
• CPC Class Codes: A01B 3/02 Man-driven ploughs
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 13/028 using expert systems only
• CPC Class Codes: G05B 19/0425 Safety, monitoring
• CPC Class Codes: G05B 19/4183 characterised by data acquisition, e.g. workpiece identification
• CPC Class Codes: G05B 19/4184 characterised by fault tolerance, reliability of production system
• CPC Class Codes: G05B 19/41845 characterised by system universality, reconfigurability, modularity
• CPC Class Codes: G05B 19/4185 characterised by the network communication
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/25428 Field device
• CPC Class Codes: G05B 2219/32287 Medical, chemical, biological laboratory
• CPC Class Codes: G05B 2219/33331 Test, diagnostic of field device for correct device, correct parameters
• CPC Class Codes: G05B 2219/35001 Data input, data handling, programming, monitoring of nc
• CPC Class Codes: G05B 2219/37337 Noise, acoustic emission, sound
• CPC Class Codes: G05B 2219/37351 Detect vibration, ultrasound
• CPC Class Codes: G05B 2219/37434 Measuring vibration of machine or workpiece or tool
• CPC Class Codes: G05B 2219/40115 Translate goal to task program, use of expert system
• CPC Class Codes: G05B 2219/45004 Mining
• CPC Class Codes: G05B 2219/45129 Boring, drilling
• CPC Class Codes: G05B 23/0221 : Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G05B 23/0229 : knowledge based, e.g. expert systems; genetic algorithms
• CPC Class Codes: G05B 23/024 : Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0264 : Control of logging system, e.g. decision on which data to store; time-stamping measurements
• CPC Class Codes: G05B 23/0283 : Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G05B 23/0286 : Modifications to the monitored process, e.g. stopping operation or adapting control
• CPC Class Codes: G05B 23/0289 : Reconfiguration to prevent failure, e.g. usually as a reaction to incipient failure detection
• CPC Class Codes: G05B 23/0291 : Switching into safety or degraded mode, e.g. protection and supervision after failure
• CPC Class Codes: G05B 23/0294 : Optimizing process, e.g. process efficiency, product quality
• CPC Class Codes: G05B 23/0297 : Reconfiguration of monitoring system, e.g. use of virtual sensors; change monitoring method as a response to monitoring results
• CPC Class Codes: G06F 18/00 : Pattern recognition
• CPC Class Codes: G06F 18/217 : Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/2178 : based on feedback of a supervisor
• CPC Class Codes: G06F 2211/005 : Network, LAN, Remote Access, Distributed System
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 20/10 : using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 : Ensemble learning
• CPC Class Codes: G06N 3/006 : based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/02 : Neural networks
• CPC Class Codes: G06N 3/042 : Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/043 : based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/047 : Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/049 : Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/084 : Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/086 : using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 3/088 : Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/126 : Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/01 : Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/046 : Forward inferencing; Production systems
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06T 11/206 : Drawing of charts or graphs
• CPC Class Codes: G06V 10/70 : using pattern recognition or machine learning optical pattern recognition or electronic computations therefor G06V10/88
• CPC Class Codes: G06V 10/776 : Validation; Performance evaluation
• CPC Class Codes: H04B 17/309 : Measuring or estimating channel quality parameters
• CPC Class Codes: H04B 17/318 : Received signal strength
• CPC Class Codes: H04L 1/0002 : by adapting the transmission rate
• CPC Class Codes: H04L 1/0041 : Arrangements at the transmitter end
• CPC Class Codes: H04L 1/18 : Automatic repetition systems, e.g. Van Duuren systems
• CPC Class Codes: H04L 12/66 : Arrangements for connecting between networks having differing types of switching systems, e.g. gateways
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 47/122 : by diverting traffic away from congested entities
• CPC Class Codes: H04L 5/0064 : Rate requirement of the data, e.g. scalable bandwidth, data priority
• CPC Class Codes: H04L 67/1097 : for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 69/00 : Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass networks security protocols H04L9/40; wireless communication networks H04W
• CPC Class Codes: H04Q 9/00 : Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
• CPC Class Codes: Y02P 80/00 : Climate change mitigation technologies for sector-wide applications
• CPC Class Codes: Y02P 80/10 : Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• CPC Class Codes: Y02P 90/02 : Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y04S 40/18 : Network protocols supporting networked applications, e.g. including control of end-device applications over a network

Abstract

The present disclosure describes a system for data collection in an industrial environment. The system can include an industrial system comprising a plurality of components each operatively coupled to a sensor, a sensor communication circuit to interpret the sensor data values in response to a sensed parameter group, a pattern recognition circuit to determine a recognized pattern value in response to a least a portion of the sensor data values, and a sensor learning circuit to update the sensed parameter group in response to the recognized pattern value, wherein the sensor communication circuit also adjusts the interpreting of the plurality of sensor data values in response to the updated sensed parameter group.

Claims

1. A system for data collection in an industrial environment, the system comprising:

an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;

a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group;

a pattern recognition circuit structured to determine a recognized pattern value in response to at least a portion of the plurality of sensor data values; and

a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value;

wherein the sensor communication circuit is further structured to adjust the interpreting of the plurality of sensor data values in response to the updated sensed parameter group,

wherein the sensor learning circuit is further structured to update the sensed parameter group by updating a network path configuration corresponding to at least one sensor from the sensed parameter group.

2. The system of claim 1, wherein the sensed parameter group comprises a fused plurality of sensors, and wherein the recognized pattern value further includes a secondary value comprising a value determined in response to the fused plurality of sensors.

3. The system of claim 2, wherein the pattern recognition circuit and the sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value.

4. The system of claim 3, wherein the sensing performance value comprises at least one performance determination selected from the performance determinations consisting of:

a signal-to-noise performance for detecting a value of interest in the industrial system;

a network utilization of the plurality of sensors in the industrial system;

an effective sensing resolution for a value of interest in the industrial system; and

a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors.

5. The system of claim 3, wherein the sensing performance value comprises a signal-to-noise performance for detecting a value of interest in the industrial system.

6. The system of claim 3, wherein the sensing performance value comprises a network utilization of the plurality of sensors in the industrial system.

7. The system of claim 3, wherein the sensing performance value comprises an effective sensing resolution for a value of interest in the industrial system.

8. The system of claim 3, wherein the sensing performance value comprises a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors.

9. The system of claim 3, wherein the sensing performance value comprises a calculation efficiency for determining the secondary value.

10. The system of claim 9, wherein the calculation efficiency comprises at least one of:

processor operations to determine the secondary value, memory utilization for determining the secondary value, a number of sensor inputs from the plurality of sensors for determining the secondary value, and supporting data long-term storage for supporting the secondary value.

11. The system of claim 3, wherein the sensing performance value comprises one of an accuracy and a precision of the secondary value.

12. The system of claim 3, wherein the sensing performance value comprises a redundancy capacity for determining the secondary value.

13. The system of claim 3, wherein the sensing performance value comprises a lead time value for determining the secondary value.

14. The system of claim 13, wherein the secondary value comprises a component overtemperature value.

15. The system of claim 13, wherein the secondary value comprises one of a component maintenance time, a component failure time, and a component service life.

16. The system of claim 13, wherein the secondary value comprises an off nominal operating condition affecting a product quality produced by an operation of the industrial system.

17. The system of claim 1, wherein the plurality of sensors comprises at least one analog sensor.

18. The system of claim 1, wherein at least one of the sensors comprises a remote sensor.

19. The system of claim 2, wherein the secondary value comprises at least one of a virtual sensor output value;

a process prediction value;

a process state value;

a component prediction value;

a component state value;

or a model output value having the sensor data values from the fused plurality of sensors as an input.

20. The system of claim 2, wherein the fused plurality of sensors further comprises at least one of a vibration sensor and a temperature sensor;

a vibration sensor and a pressure sensor;

a vibration sensor and an electric field sensor;

a vibration sensor and a heat flux sensor;

a vibration sensor and a galvanic sensor;

or a vibration sensor and a magnetic sensor.

21. The system of claim 1, wherein the sensor learning circuit is further structured to update the sensed parameter group to form the updated sensed parameter group by performing at least one additional operation selected from the operations consisting of:

updating a sensor selection of the sensed parameter group;

updating a sensor sampling rate of at least one sensor from the sensed parameter group;

updating a sensor resolution of at least one sensor from the sensed parameter group;

updating a storage value corresponding to at least one sensor from the sensed parameter group;

updating a priority corresponding to at least one sensor from the sensed parameter group; and

updating at least one of a sampling rate, sampling order, or sampling phase.

22. The system of claim 21, wherein the pattern recognition circuit is further structured to determine the recognized pattern value by at least one of determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest;

determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;

determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;

determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;

determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;

determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;

or updating the recognized pattern value in response to external feedback.

23. The system of claim 22, wherein the value of interest comprises at least one value selected from the values consisting of:

a virtual sensor output value;

a process prediction value;

a process state value;

a component prediction value;

a component state value; and

a model output value having the sensor data values from a fused plurality of sensors as an input.

24. The system of claim 1, wherein the pattern recognition circuit is further structured to access cloud-based data comprising a second plurality of sensor data values, the second plurality of sensor data values corresponding to at least one offset industrial system.

25. The system of claim 24, wherein the sensor learning circuit is further structured to access the cloud-based data comprising a second updated sensor parameter group corresponding to the at least one offset industrial system.