Local Analytics at an Asset

• US 20160371584A1
• Filed: 12/08/2015
• Published: 12/22/2016
• Est. Priority Date: 06/05/2015
• Status: Active Grant

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake Technologies, Inc.
• Inventors: Nicholas, Brad, Kolb, Jason
• Granted Patent: US 10,254,751 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 23/02 Electric testing or monitoring
• CPC Class Codes: G05B 23/0213 Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
• CPC Class Codes: G05B 23/0286 Modifications to the monitored process, e.g. stopping operation or adapting control
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: Y02P 90/30 Computing systems specially adapted for manufacturing

Abstract

Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Claims

1. A computing device comprising:

an asset interface configured to couple the computing device to an asset;

a network interface configured to facilitate communication between the computing device and a computing system located remote from the computing device;

at least one processor;

a non-transitory computer-readable medium; and

program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing device to;

receive, via the network interface, a predictive model that is related to the operation of the asset, wherein the predictive model is defined by the computing system based on operating data for a plurality of assets;

receive, via the asset interface, operating data for the asset;

execute the predictive model based on at least a portion of the received operating data for the asset; and

based on executing the predictive model, execute a workflow corresponding to the predictive model, wherein executing the workflow comprises causing the asset, via the asset interface, to perform an operation.

2. The computing device of claim 1, wherein the asset interface communicatively couples the computing device to an on-asset computer of the asset.

3. The computing device of claim 1, wherein the asset comprises an actuator, and wherein executing the workflow comprises causing the actuator to perform a mechanical operation.

4. The computing device of claim 1, wherein executing the workflow comprises causing the asset to execute a diagnostic tool.

5. The computing device of claim 1, wherein executing the workflow further comprises causing, via the network interface, execution of an operation remote from the asset.

6. The computing device of claim 5, wherein causing execution of an operation remote from the asset comprises instructing the computing system to execute an operation remote from the asset.

7. The computing device of claim 1, wherein the program instructions stored on the non-transitory computer-readable medium are further executable by the at least one processor to cause the computing device to:

before executing the predictive model, individualize the predictive model.

8. The computing device of claim 7, wherein individualizing the predictive model comprises modifying one or more parameters of the predictive model based at least on received operating data for the asset.

9. The computing device of claim 7, wherein the program instructions stored on the non-transitory computer-readable medium are further executable by the at least one processor to cause the computing device to:

after individualizing the predictive model, transmit to the computing system, via the network interface, an indication that the predictive model has been individualized.

10. The computing device of claim 1, wherein the predictive model is a first predictive model, and wherein the program instructions stored on the non-transitory computer-readable medium are further executable by the at least one processor to cause the computing device to:

before executing the first predictive model, transmit to the computing system, via the network interface, a given subset of the received operating data for the asset, wherein the given subset of received operating data comprises operating data generated by a given group of one or more sensors.

11. The computing device of claim 10, wherein the program instructions stored on the non-transitory computer-readable medium are further executable by the at least one processor to cause the computing device to:

after transmitting the given subset of the received operating data for the asset, receive a second predictive model that is related to the operation of the asset, wherein the second predictive model is defined by the computing system based on the given subset of the received operating data for the asset; and

execute the second predictive model instead of the first predictive model.

12. A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing device coupled to an asset via an asset interface of the computing device to:

receive, via a network interface of the computing device configured to facilitate communication between the computing device and a computing system located remote from the computing device, a predictive model that is related to the operation of the asset, wherein the predictive model is defined by the computing system based on operating data for a plurality of assets;

receive, via the asset interface, operating data for the asset;

execute the predictive model based on at least a portion of the received operating data for the asset; and

based on executing the predictive model, execute a workflow corresponding to the predictive model, wherein executing the workflow comprises causing the asset, via the asset interface, to perform an operation.

13. The non-transitory computer-readable medium of claim 12, wherein the program instructions stored on the non-transitory computer-readable medium are further executable to cause the computing device to:

before executing the predictive model, individualize the predictive model.

14. The non-transitory computer-readable medium of claim 13, wherein individualizing the predictive model comprises modifying one or more parameters of the predictive model based at least on received operating data for the asset.

15. The non-transitory computer-readable medium of claim 12, wherein the predictive model is a first predictive model, and wherein the program instructions stored on the non-transitory computer-readable medium are further executable to cause the computing device to:

before executing the first predictive model, transmit to the computing system, via the network interface, a given subset of the received operating data for the asset, wherein the given subset of received operating data comprises operating data generated by a given group of one or more sensors.

16. The non-transitory computer-readable medium of claim 15, wherein the program instructions stored on the non-transitory computer-readable medium are further executable to cause the computing device to:

after transmitting the operating data from the particular group of the one or more sensors, receive a second predictive model that is related to the operation of the asset, wherein the second predictive model is defined by the computing system based on the given subset of the received operating data for the asset; and

execute the second predictive model instead of the first model.

17. A computer-implemented method, the method comprising:

receiving, via a network interface of a computing device that is coupled to an asset via an asset interface of the computing device, a predictive model that is related to the operation of the asset, wherein the predictive model is defined by a computing system located remote from the computing device based on operating data for a plurality of assets;

receiving, by the computing device via the asset interface, operating data for the asset;

executing, by the computing device, the predictive model based on at least a portion of the received operating data for the asset; and

based on executing the predictive model, executing, by the computing device, a workflow corresponding to the predictive model, wherein executing the workflow comprises causing the asset, via the asset interface, to perform an operation.

18. The computer-implemented method of claim 17, the method further comprising:

before executing the predictive model, individualizing, by the computing device, the predictive model.

19. The computer-implemented method of claim 18, wherein individualizing the predictive model comprises modifying one or more parameters of the predictive model based at least on received operating data for the asset.

20. The computer-implemented method of claim 17, wherein executing the workflow further comprises causing, via the network interface, execution of an operation remote from the asset.