Systems and methods for data collection and data sharing in an industrial environment

• US 10,678,233 B2
• Filed: 06/29/2019
• Issued: 06/09/2020
• 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): Patel, Kamini B
• Application Number:
  US16/458,053
• Publication Number:
  US 20190324434A1
• Time in Patent Office: 346 Days
• Field of Search: 714 3
• 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 systems for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process. A system can include a data circuit for analyzing a plurality of sensor inputs, a network control circuit for sending and receiving information related to sensor inputs to an external system, wherein the system provides sensor data to one or more similarly configured systems, and wherein the data circuit dynamically nominates a similarly configured system capable of providing sensor data to replace the system.

Claims

1. A system for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process, the system comprising:

a data circuit for analyzing a plurality of sensor inputs;

a network control circuit for sending and receiving information related to the plurality of sensor inputs to an external system;

wherein the system provides sensor data to one or more similarly configured systems; and

wherein the data circuit dynamically nominates a similarly configured system capable of providing sensor data to replace the system.

2. The system of claim 1, wherein the nomination is triggered by detection of a system failure mode.

3. The system of claim 1, wherein, when the system is unable to supply a requested signal, it nominates another similarly configured system to supply similar but not identical information to a requestor.

4. The system of claim 3, wherein the system indicates to the requestor that a new signal is different than an original signal.

5. The system of claim 1, wherein the network control circuit further implements a network of similarly configured systems using an intercommunication protocol selected from a list comprising:

multi-hop, mesh, serial, parallel, ring, real-time and hub-and-spoke.

6. The system of claim 1, wherein, after a configurable time period, the system stores only digests of the information and discards underlying information.

7. The system of claim 1, wherein the network control circuit self-arranges the system into a redundant storage network with one or more similarly configured systems.

8. The system of claim 1, wherein the network control circuit self-arranges the system into a fault-tolerant storage network with one or more similarly configured systems.

9. The system of claim 1, wherein the network control circuit self-arranges the system into a hierarchical storage network with one or more similarly configured systems.

10. The system of claim 1, wherein the network control circuit self-arranges the system into a hierarchical data transmission configuration to reduce upstream traffic.

11. The system of claim 1, wherein the network control circuit self-arranges the system into a matrixed network configuration with multiple redundant data paths to increase reliability of information transmission.

12. The system of claim 1, wherein the network control circuit self-arranges the system into a matrixed network configuration with multiple redundant data paths to increase reliability of information transmission.

13. The system of claim 1, wherein the system accumulates data received from other similarly configured systems while an upstream network connection is unavailable, and then sends all accumulated data once the upstream network connection is restored.

14. The system of claim 1, wherein accumulated data is committed to a remote database.

15. The system of claim 1, wherein the system rearranges its position in a mesh network topology with other similarly configured systems in order to minimize an amount of data it must relay from the other similarly configured systems.

16. The system of claim 1, wherein the system rearranges its position in a mesh network topology with other similarly configured systems in order to minimize an amount of data it must send through the other similarly configured systems.

17. A system for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process, the system comprising:

a data circuit for analyzing a plurality of sensor inputs;

a network control circuit for sending and receiving information related to the plurality of sensor inputs to an external system;

wherein the system provides sensor data to one or more similarly configured systems; and

wherein the system and the one or more similarly configured systems are arranged as a consolidated virtual information provider.

18. The system of claim 17, wherein the system and each of the one or more similarly configured systems multiplex their information.

19. The system of claim 18, wherein the system and each of the one or more similarly configured systems provide a single unified information source to a requestor.

20. The system of claim 17, wherein the system and each of the one or more similarly configured systems further comprise an intelligent agent circuit that combines the data between systems.

21. The system of claim 17, wherein the system and each of the one or more similarly configured systems further comprise an intelligent agent circuit that chooses what data to collect or store based on a machine learning algorithm.

22. The system of claim 21, wherein the machine learning algorithm further comprises a feedback function that takes as input what data is used by an external system.

23. The system of claim 21, wherein the machine learning algorithm further comprises a control function that adjusts a degree of precision, frequency of capture, or information stored based on an analysis of requests for data over time.

24. The system of claim 21, wherein the machine learning algorithm further comprises a feedback function that adjusts what sensor data is captured based on an analysis of requests for information over time.

25. The system of claim 21, wherein the machine learning algorithm further comprises a feedback function that adjusts what sensor data is captured based on historical use of information.

26. The system of claim 21, wherein the machine learning algorithm further comprises a feedback function that adjusts what sensor data is captured based on what information was most indicative of a failure mode.

27. The system of claim 21, wherein the machine learning algorithm further comprises a feedback function that adjusts what sensor data is captured based on detected combinations of information coincident with a failure mode.

28. The system of claim 17, wherein the network control circuit implements a network of similarly configured systems using an intercommunication protocol selected from a list comprising:

multi-hop, mesh, serial, parallel, ring, real-time and hub-and-spoke.

29. The system of claim 17, wherein the network control circuit self-arranges the system into network communication with similarly configured systems using an intercommunication protocol selected from a list comprising:

multi-hop, mesh, serial, parallel, ring, real-time and hub-and-spoke.

30. The system of claim 17, wherein, after a configurable time period, the system stores only digests of the information and discards underlying information.