Local analytics at an asset

Patent metadata

Field	Value
Publication number	US-10254751-B2
Application number	US-201514963207-A
Country	US
Kind code	B2
Filing date	Dec 8, 2015
Priority date	Jun 5, 2015
Publication date	Apr 9, 2019
Grant date	Apr 9, 2019

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.

First claim

The invention claimed is:

1. A computing device that is capable of being physically coupled to an asset, the computing device comprising:
an asset interface configured to communicatively couple the computing device to one or more on-board components of the asset;
a network interface configured to facilitate wireless, network-based 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 that is (a) physically coupled to an asset and (b) communicatively coupled to one or more on-board components of the asset via an asset interface of the computing device to:
receive, via a network interface of the computing device configured to facilitate wireless, network-based 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, by a computing device that is (a) physically coupled to an asset and (b) communicatively coupled to one or more on-board components of the 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

Assignees

• Uptake Tech Inc

Inventors

• Nicholas Brad
• Kolb Jason

Classifications

• Y02P90/30

  Computing systems specially adapted for manufacturing · CPC title

• G05B23/0286

  Modifications to the monitored process, e.g. stopping operation or adapting control · CPC title

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G05B23/02 Primary

  Electric testing or monitoring · CPC title

• G05B13/048

  using a predictor · CPC title