Methods and systems for the industrial internet of things

Patent metadata

Field	Value
Publication number	US-11774944-B2
Application number	US-201816185625-A
Country	US
Kind code	B2
Filing date	Nov 9, 2018
Priority date	May 9, 2016
Publication date	Oct 3, 2023
Grant date	Oct 3, 2023

Abstract

An example data collection system in an industrial environment includes a data collector in communication with a number of input channels for acquiring collected data. The system includes a data storage that stored the collected data as a number of data pools. The system includes a self-organizing data marketplace engine that receives the data pools, and that is organized based on training a marketplace self-organization with a training set, and further based on feedback from measures of marketplace success with respect to the data pools.

First claim

What is claimed is:

1. A monitoring system for data collection in an industrial environment, the system comprising:
a plurality of sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and
wherein the at least one collection parameter is a bandwidth parameter.

2. The system of claim 1 , wherein the state corresponds to an outcome relating to a machine in the industrial environment.

3. The system of claim 1 , wherein the state corresponds to an anticipated outcome relating to a machine in the industrial environment.

4. The system of claim 1 , wherein the state corresponds to an outcome relating to a process in the industrial environment.

5. The system of claim 1 , wherein the state corresponds to an anticipated outcome relating to a process in the industrial environment.

6. The system of claim 1 , wherein the at least one collection parameter is used to govern a multiplexing of the plurality of the sensors.

7. The system of claim 1 , wherein the at least one collection parameter is a timing parameter.

8. The system of claim 1 , wherein the at least one collection parameter relates to a frequency range.

9. The system of claim 1 , wherein the at least one collection parameter relates to a granularity of a collection of sensor data.

10. The system of claim 1 , wherein the at least one collection parameter is a storage parameter for the collected output data.

11. The system of claim 1 , wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model.

12. The system of claim 11 , wherein the model is a physical model, an operational model, or a system model.

13. The system of claim 1 , wherein the machine learning data analysis circuit is structured to learn received output data patterns based on the state.

14. The system of claim 13 , wherein the data collection band circuit alters at least one subset of the plurality of sensors when the learned received output data pattern does not reliably predict the state.

15. The system of claim 14 , wherein altering the at least one subset comprises discontinuing collection of data from the at least one subset.

16. A monitoring device for data collection in an industrial environment, the monitoring device comprising:
a plurality of sensors communicatively coupled to a controller, the controller comprising:
a data collection band circuit structured to determine at least one subset of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one subset of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters an aspect of the at least one subset of the plurality of sensors based on one or more of the learned received output data patterns and the state, and
wherein the aspect that the data collection band circuit alters is a number of data points collected from one or more members of the at least one subset of the plurality of sensors.

17. The monitoring device of claim 16 , wherein the aspect that the data collection band circuit alters is a frequency of data points collected from the one or more members of the at least one subset of plurality of sensors.

18. The monitoring device of claim 16 , wherein the aspect that the data collection band circuit alters is a bandwidth parameter.

19. The monitoring device of claim 16 , wherein the aspect that the data collection band circuit alters is a timing parameter.

20. The monitoring device of claim 16 , wherein the aspect that the data collection band circuit alters relates to a frequency range.

21. The monitoring device of claim 16 , wherein the aspect that the data collection band circuit alters relates to a granularity of collection of sensor data.

22. The monitoring device of claim 16 , wherein the altered aspect is a storage parameter for the collected output data.

23. A monitoring system for data collection in an industrial environment, the system comprising:
a plurality of sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and
wherein the collection parameter is used to govern a multiplexing of a plurality of the sensors.

24. The system of claim 23 , wherein the state corresponds to an outcome relating to a machine in the environment.

25. The system of claim 23 , wherein the state corresponds to at least one of: an anticipated outcome relating to a machine in the environment, an outcome relating to a process in the environment, or an anticipated outcome relating to a process in the environment.

26. The system of claim 23 wherein the collection parameter is at least one of: a bandwidth parameter, a storage parameter for the collected data, or a timing parameter.

27. The system of claim 23 , wherein the collection parameter relates to at least one of: a frequency range, a granularity of collection of sensor data.

28. The system of claim 23 , wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model, wherein the model is a physical model, an operational model, or a system model.

29. The system of claim 23 , wherein the machine learning data analysis circuit is structured to learn received output data patterns based on the state.

30. The system of claim 23 , wherein the data collection band circuit alters at least one subset of the plurality of sensors when the learned received output data pattern does not reliably predict the state, and wherein altering the at least one subset comprises discontinuing collection of data from the at least one subset.

31. A monitoring system for data collection in an industrial environment, the system comprising:
a plurality of sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality o

Assignees

• Strong Force Iot Portfolio 2016 Llc

Inventors

• Cella Charles Howard
• Desai Mehul
• Duffy Jr Gerald William
• Mcguckin Jeffrey P
• Ho Tracey
• Segui John
• Blumenthal Steven
• Meng Chun

Classifications

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title