SYSTEMS AND METHODS FOR NETWORK-SENSITIVE DATA COLLECTION

• US 20190324440A1
• Filed: 06/30/2019
• Published: 10/24/2019
• 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
• Granted Patent: US 10,824,140 B2
• Time in Patent Office: Days
• 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 self-organized, network-sensitive data collection in an industrial environment. A system may include an industrial system with a plurality of components operatively coupled to sensors, a sensor communication circuit to interpret the data values from the sensors, and a system collaboration circuit to communicate at least a portion of the data values over a network to a storage target computing device according to a sensor data transmission protocol. A transmission environment circuit may determine transmission feedback corresponding to the communication of the data values over the network, and a network management circuit to update the sensor data transmission protocol in response to the transmission feedback.

Claims

1. A system for self-organized, network-sensitive data collection in an industrial environment, the system comprising:

an industrial system including 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 from the plurality of sensors;

a system collaboration circuit structured to communicate at least a portion of the plurality of sensor data values over a network having a plurality of nodes to a storage target computing device according to a sensor data transmission protocol;

a transmission environment circuit structured to determine transmission feedback corresponding to the communication of the at least a portion of the plurality of sensor data values over the network; and

a network management circuit structured to update the sensor data transmission protocol in response to the transmission feedback, wherein the system collaboration circuit is further responsive to the updated sensor data transmission protocol.

2. The system of claim 1, wherein the system collaboration circuit is further structured to send an alert to at least one of the plurality of nodes in response to the updated sensor data transmission protocol.

3. The system of claim 1, wherein updating the sensor data transmission protocol comprises at least one operation selected from the operations consisting of providing instructions to rearrange a mesh network including the plurality of nodes, providing instructions to rearrange a hierarchical data network including the plurality of nodes, rearranging a peer-to-peer data network including the plurality of nodes and rearranging a hybrid peer-to-peer data network including the plurality of nodes.

4. The system of claim 1, wherein updating the sensor data transmission protocol comprises at least one operation selected from the operations consisting of:

providing instructions to reduce a quantity of data sent over the network, providing instructions to adjust a frequency of data capture sent over the network, providing instructions to time-shift delivery of at least a portion of the plurality of sensor data values sent over the network, and providing instructions to change a network protocol corresponding to the network.

5. The system of claim 1, wherein updating the sensor data transmission protocol comprises at least one operation selected from the operations consisting of providing instructions to reduce a throughput of at least one device coupled to the network, providing instructions to reduce a bandwidth use of the network, providing instructions to compress data corresponding to at least a portion of the plurality of sensor data values sent over the network, providing instructions to condense data corresponding to at least a portion of the plurality of sensor values sent over the network, providing instructions to summarize data corresponding to at least a portion of the plurality of sensor values sent over the network, and providing instructions to encrypt data corresponding to at least a portion of the plurality of sensor data values sent over the network.

6. The system of claim 1, wherein updating the sensor data transmission protocol comprises at least one operation selected from the operations consisting of:

providing instructions to deliver data corresponding to at least a portion of the plurality of sensor data values to a distributed ledger;

providing instructions to deliver data corresponding to at least a portion of the plurality of sensor data values to a central server;

providing instructions to deliver data corresponding to at least a portion of the plurality of sensor data values to a super-node; and

providing instructions to deliver data corresponding to at least a portion of the plurality of sensor data values redundantly across a plurality of network connections.

7. The system of claim 1, wherein updating the sensor data transmission protocol comprises providing instructions to deliver data corresponding to at least a portion of the plurality of sensor data values to one of the plurality of components.

8. The system of claim 1, wherein the one of the plurality of components is communicatively coupled to the sensor providing the data corresponding to at least a portion of the plurality of sensor data values.

9. The system of claim 1, wherein the system collaboration circuit is further structured to interpret a quality of service commitment, and wherein the network management circuit is further structured to update the sensor data transmission protocol further in response to the quality of service commitment.

10. The system of claim 1, wherein the system collaboration circuit is further structured to interpret a service level agreement, and wherein the network management circuit is further structured to update the sensor data transmission protocol further in response to the service level agreement.

11. The system of claim 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to provide instructions to increase a quality of service value.

12. The system of claim 1, wherein the network comprises a mesh network, and wherein the network management circuit is further structured to update the sensor data transmission protocol to provide instructions to eject one of the plurality of nodes from the mesh network.

13. The system of claim 1, wherein the network comprises a peer-to-peer network, and wherein the network management circuit is further structured to update the sensor data transmission protocol to provide instructions to eject one of the plurality of nodes from the peer-to-peer network.

14. The system of claim 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to cache at least a portion of the plurality of sensor data values to obtain cached data.

15. The system of claim 14, wherein the network management circuit is further structured to update the sensor data transmission protocol to communicate the cached data in response to at least one of:

a determination that the cached data is requested, a determination that the network feedback indicates communication of the cached data is available, and a determination that higher priority data is present that requires utilization of cache resources holding the cached data.

16. The system of claim 1, wherein the system further includes a data collector configured to receive the at least a portion of the plurality of sensor data values, wherein the at least a portion of the plurality of sensor data values includes data provided by a plurality of the sensors, and wherein the transmission feedback includes network performance information corresponding to the data collector.

17. The system of claim 1, wherein the system further comprises a data collector configured to receive the at least a portion of the plurality of sensor data values, wherein the at least a portion of the plurality of sensor data values includes data provided by a plurality of the sensors, a second data collector communicatively coupled to the network, and wherein the transmission feedback includes network performance information corresponding to the second data collector.

18. A system for self-organized, network-sensitive data collection in an industrial environment, the system comprising:

an industrial system including 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 from the plurality of sensors at a predetermined frequency;

a system collaboration circuit structured to communicate at least a portion of the plurality of sensor data values over a network having a plurality of nodes to a storage target computing device according to a sensor data transmission protocol, the sensor data transmission protocol comprising a predetermined hierarchy of data collection and the predetermined frequency;

a transmission environment circuit structured to determine transmission feedback corresponding to the communication of the at least a portion of the plurality of sensor data values over the network; and

a network management circuit structured to update the sensor data transmission protocol in response to the transmission feedback and further in response to benchmarking data, wherein the system collaboration circuit is further responsive to the updated sensor data transmission protocol.

19. The system of claim 18, wherein updating the sensor data transmission protocol comprises at least one operation selected from a list of operations comprising:

providing an instruction to change the sensors of the plurality of sensors;

providing an instruction to adjust the predetermined frequency;

providing an instruction to adjust a quantity of the plurality of sensor data values that are stored;

providing an instruction to adjust a data transmission rate of the communication of the at least a portion of the plurality of sensor data values;

providing an instruction to adjust a data transmission time of the communication of the at least a portion of the plurality of sensor data values; and

providing an instruction to adjust a networking method of the communication over the network.

20. The system of claim 18, wherein the benchmarking data further comprises data selected from a list comprising:

a network efficiency, a data efficiency, a comparison with offset data collectors, a throughput efficiency, a data efficacy, a data quality, a data precision, a data accuracy, and a data frequency.

21. The system of claim 18, wherein the benchmarking data further comprises data selected from a list comprising:

an environmental response, a mesh networking coherence, a data coverage, a target coverage, a signal diversity, a critical response, and a motion.

22. The system of claim 18, wherein the benchmarking data further includes data selected from the list consisting of a quality of service commitment, a quality of service guarantee, a service level agreement, and a predetermined quality of service value.

23. The system of claim 18, wherein the benchmarking data further includes data selected from a list comprising:

a network interference value, a network obstruction value, and an area of impeded network connectivity.

24. The system of claim 18, wherein the transmission feedback includes a communication interference value selected from a list of values comprising:

an interference caused by a component of the system;

an interference caused by one of the sensors;

an interference caused by a metallic object;

an interference caused by a physical obstruction;

an attenuated signal caused by a low power condition; and

an attenuated signal caused by a network traffic demand in a portion of the network.

25. A system for self-organized, network-sensitive data collection in an industrial environment, the system comprising:

an industrial system including 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 from the plurality of sensors at a predetermined frequency;

a system collaboration circuit structured to communicate at least a portion of the plurality of sensor data values over a network having a plurality of nodes to a storage target computing device according to a sensor data transmission protocol;

a transmission environment circuit structured to determine transmission feedback corresponding to the communication of the at least a portion of the plurality of sensor data values over the network;

a network management circuit structured to update the sensor data transmission protocol in response to the transmission feedback; and

a network notification circuit structured to provide an alert value in response to the updated sensor data transmission protocol, wherein the system collaboration circuit is further responsive to the updated sensor data transmission protocol.

26. The system of claim 25, wherein the transmission feedback includes at least one feedback value selected from a list of values comprising:

a change in transmission pricing, a change in storage pricing, a loss of connectivity, a reduction of bandwidth, a change in connectivity, a change in network availability, a change in network range, a change in wide area network (WAN) connectivity, and a change in wireless local area network (WLAN) connectivity.

27. The system of claim 25, wherein the network management circuit further comprises an expert system, and wherein the updating the sensor data transmission protocol is further in response to operations of the expert system.

28. The system of claim 27, wherein the expert system comprises at least one system selected from a list of systems comprising:

a rule-based system, a model-based system, a neural-net system, a Bayesian-based system, a fuzzy logic-based system, and a machine learning system.

29. The system of claim 25, wherein the network management circuit further comprises a machine learning algorithm, and wherein the updating the sensor data transmission protocol is further in response to operations of the machine learning algorithm.

30. The system of claim 29, wherein the machine learning algorithm is further structured to utilize feedback data including the transmission conditions.

31. The system of claim 30, wherein the feedback data further comprises at least a portion of the plurality of sensor data values.

32. The system of claim 25, wherein the feedback data further comprises benchmarking data.

33. The system of claim 32, wherein the benchmarking data further comprises data selected from a list consisting of:

a network efficiency, a data efficiency, a comparison with offset data collectors, a throughput efficiency, a data efficacy, a data quality, a data precision, a data accuracy, a data frequency, an environmental response, a mesh networking coherence, a data coverage, a target coverage, a signal diversity, a critical response, and a motion efficiency.