Computer Architecture and Method for Recommending Asset Repairs

• US 20180039956A1
• Filed: 08/08/2016
• Published: 02/08/2018
• Est. Priority Date: 08/08/2016
• Status: Abandoned Application

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake Technologies, Inc.
• Inventors: McElhinney, Adam, Silva, Brian, Lim, Jung Ha
• Application Number:
  US15/231,587
• Publication Number:
  US 20180039956A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

Disclosed herein are systems, devices, and methods related for generating a recommendation to repair an asset based on operating data. A computing system may be configured maintain a hierarchy that comprises two or more distinct levels of conditions that operating data may be checked against in order to determine which repair recommendation (if any) should be output. The hierarchy may include at least (1) a first condition that corresponds to a first repair recommendation having a first level of precision, and (2) a second condition that corresponds to a second repair recommendation having a second level of precision. Once repair recommendations are identified for satisfied conditions, the computer system may select the recommendation having the highest level of precision and then cause that recommendation to be output.

Claims

1. A computing system comprising:

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 system to;

maintain a hierarchy of conditions that correspond to recommendations for repairing an asset based on operating data, wherein the hierarchy comprises at least (1) a first condition that is based on a predefined rule and corresponds to a first repair recommendation having a first level of precision and (2) a second condition that is based on a predictive model and corresponds to a second repair recommendation having a second level of precision, wherein the first and second levels of precision differ;

receive operating data for a given asset of a plurality of assets;

determine that the first and second conditions of the hierarchy are satisfied by the received operating data and thereby identifying the first and second recommendations;

identify which one of the first and second recommendations has a higher level of precision; and

cause a computing device to output an indication of the identified one of the first and second recommendations.

2. The computing system of claim 1, wherein the hierarchy further comprises a third condition that corresponds to a third repair recommendation having a third level of precision.

3. The computing system of claim 2, wherein the third level of precision is the same as either the first level of precision or the second level of precision.

4. The computing system of claim 1, wherein the program instructions that are executable by the at least one processor to cause the computing system to cause the computing device to determine that the first condition is satisfied by the received operating data comprise program instructions that are executable by the at least one processor to cause the computing system to:

determine that the received operating data satisfies the predefined rule;

identify a confidence level associated with satisfaction of the predefined rule; and

determine that the identified confidence level exceeds a confidence level threshold.

5. The computing system of claim 4, wherein the confidence level associated with the predefined rule is based at least in part on user input.

6. The computing system of claim 1, wherein the program instructions that are executable by the at least one processor to cause the computing device to determine that the second condition is satisfied by the received operating data comprise program instructions that are executable by the at least one processor to cause the computing device to:

apply the predictive model to the received operation data; and

determine that an output of the predictive model exceeds a confidence level threshold.

7. The computing system of claim 1, wherein the predictive model comprises a predictive model for outputting an indication of a likelihood that a given repair is needed at an asset based on operating data for the asset.

8. The computing system of claim 1, wherein the predictive model is defined based at least on historical repair data and historical operating data for a plurality of assets.

9. A non-transitory computer-readable medium having program instructions stored thereon that are executable to cause a computing device to:

maintain a hierarchy of conditions that correspond to recommendations for repairing an asset based on operating data, wherein the hierarchy comprises at least (1) a first condition that is based on a predefined rule and corresponds to a first repair recommendation having a first level of precision and (2) a second condition that is based on a predictive model and corresponds to a second repair recommendation having a second level of precision, wherein the first and second levels of precision differ;

receive operating data for a given asset of a plurality of assets;

determine that the first and second conditions of the hierarchy are satisfied by the received operating data and thereby identifying the first and second recommendations;

identify which one of the first and second recommendations has a higher level of precision; and

cause a computing device to output an indication of the identified one of the first and second recommendations.

10. The non-transitory computer-readable medium of claim 9, wherein the hierarchy further comprises a third condition that corresponds to a third repair recommendation having a third level of precision.

11. The non-transitory computer-readable medium of claim 10, wherein the third level of precision is the same as either the first level of precision or the second level of precision.

12. The non-transitory computer-readable medium of claim 9, wherein the program instructions that are executable to cause a computing device to determine that the first condition is satisfied by the received operating data comprise program instructions that are executable to cause a computing device to:

determine that the received operating data satisfies the predefined rule;

identify a confidence level associated with the predefined rule; and

determine that the identified confidence level exceeds a confidence level threshold.

13. The non-transitory computer-readable medium of claim 12, wherein the confidence level associated with the predefined rule is based at least in part on user input.

14. The non-transitory computer-readable medium of claim 9, wherein the program instructions that are executable to cause a computing device to determine that the second condition is satisfied by the received operating data comprise program instructions that are executable to cause a computing device to:

apply the predictive model to the received operation data; and

determine that an output of the predictive model exceeds a confidence level threshold.

15. The non-transitory computer-readable medium of claim 9, wherein the predictive model comprises a predictive model for outputting an indication of a likelihood that a given repair is needed at an asset based on operating data for the asset.

16. A computer-implemented method comprising:

maintaining a hierarchy of conditions for generating a recommendation based on operating data for an asset, wherein the hierarchy comprises at least (1) a first condition that is based on a predefined rule and corresponds to a first repair recommendation having a first level of precision and (b) a second condition that is based on a predictive model and corresponds to a second repair recommendation having a second level of precision, wherein the first and second levels of precision differ;

receiving operating data for a given asset of a plurality of assets; and

determining that the first and second recommendation are satisfied by the received operating data and thereby identifying the first and second recommendations;

identifying which one of the first and second recommendations has a higher level of precision; and

causing a computing device to output an indication of the identified on of the first and second recommendations.

17. The computer-implemented method of claim 16, wherein determining that the first condition is satisfied by the received operating data comprises:

determining that the received operating data satisfies the predefined rule;

identifying a confidence level associated with the predefined rule; and

determining that the identified confidence level exceeds a confidence level threshold.

18. The computer-implemented method of claim 16, wherein determining that the second condition is satisfied by the received operating comprises:

applying the predictive model to the received operation data; and

determining that an output of the predictive model exceeds a confidence level threshold.

19. The computer-implemented method of claim 16, wherein the predictive model comprises a predictive model for outputting an indication of a likelihood that a given repair is needed at an asset based on operating data for the asset.

20. The computer-implemented method of claim 16, wherein the predictive model is defined based at least on historical repair data and historical operating data for a plurality of assets.