Asset Health Score

• US 20160153806A1
• Filed: 06/05/2015
• Published: 06/02/2016
• Est. Priority Date: 12/01/2014
• Status: Active Grant

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake LLC
• Inventors: Ciasulli, John, Horrell, Michael, Zhong, Sheng, Kolb, Jason
• Granted Patent: US 10,417,076 B2
• Time in Patent Office: Days
• CPC Class Codes: G01D 3/08 with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
• CPC Class Codes: G01M 99/005 Testing of complete machines, e.g. washing-machines or mobile phones testing of machine parts G01M13/00; testing of electric apparatus or components G01R31/50
• CPC Class Codes: G01M 99/008 by doing functionality tests
• CPC Class Codes: G05B 19/18 Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form G05B19/418 takes precedence
• 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 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0275 Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0721 within a central processing unit [CPU]
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/0754 by exceeding limits
• CPC Class Codes: G06F 11/0772 Means for error signaling, e.g. using interrupts, exception flags, dedicated error registers
• CPC Class Codes: G06F 11/0787 Storage of error reports, e.g. persistent data storage, storage using memory protection
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/0793 Remedial or corrective actions recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40
• CPC Class Codes: G06F 11/2007 using redundant communication media
• CPC Class Codes: G06F 11/26 Functional testing
• CPC Class Codes: G06F 11/263 Generation of test inputs, e.g. test vectors, patterns or sequences ; with adaptation of the tested hardware for testability with external testers
• CPC Class Codes: G06F 2201/85 Active fault masking without idle spares active fault masking without idle spare hardware where processing functionality is redundant G06F11/2035
• CPC Class Codes: G06N 5/02 : Knowledge representation; Symbolic representation
• 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 10/06312 : Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/0633 : Workflow analysis
• CPC Class Codes: G06Q 10/067 : Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/20 : Administration of product repair or maintenance
• CPC Class Codes: G06Q 50/04 : Manufacturing
• CPC Class Codes: G06Q 50/08 : Construction
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0825 : using optical means
• CPC Class Codes: G08B 21/18 : Status alarms G08B21/02 takes precedence
• CPC Class Codes: H04L 45/22 : Alternate routing
• CPC Class Codes: Y02P 90/30 : Computing systems specially adapted for manufacturing

Abstract

Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators, among other examples.

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;

based at least on historical operating data for a plurality of assets, define a predictive model for outputting a health metric indicating whether at least one failure from a group of failures is likely to occur at an asset within a given period of time in the future, wherein the historical operating data comprises (i) historical abnormal-condition data associated with one or more failures that occurred at the plurality of assets in the past and (ii) historical sensor data indicating operating conditions of the plurality of assets in the past;

receive sensor data indicating at least one operating condition of a given asset;

based on the received sensor data, execute the predictive model to determine, for the given asset, a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future; and

transmit, to a computing device, health-metric data indicating the determined health metric for the given asset to facilitate causing the computing device to display a representation of the determined health metric for the given asset.

2. The computing system of claim 1, wherein the group of failures comprises one or more failures that could render the given asset inoperable when the one or more failures occur.

5. The computing system of claim 1, wherein the health metric for the given asset comprises one of (i) a probability that no failure from the group of failures will occur at the given asset within the given period of time in the future or (ii) a probability that at least one failure from the group of failures will occur at the given asset within the given period of time in the future.

6. The computing system of claim 1, wherein the program instructions are further executable by the at least one processor to cause the computing system to:

determine that the health metric for the given asset is below a threshold value; and

based on determining that the health metric for the given asset is below a threshold value, cause an action to be performed to facilitate increasing the health metric for the given asset.

7. The computing system of claim 6, wherein causing the action comprises causing the computing device to output an alert indicating a decrease in the health metric for the given asset.

8. The computing system of claim 6, wherein causing the action comprises causing the computing device to output an indication of one or more recommended repairs to the given asset.

9. The computing system of claim 6, wherein causing the action comprises causing the given asset to modify one or more operating conditions.

10. The computing system of claim 6, wherein the action is one action from a plurality of actions, and wherein the program instructions are further executable by the at least one processor to cause the computing system to:

after causing the action, determine feedback data indicating a status of the action; and

based on the feedback data, prioritize the action over other actions from the plurality of actions.

3. (canceled)

4. (canceled)

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

based at least on historical operating data for a plurality of assets, define a predictive model for outputting a health metric indicating whether at least one failure from a group of failures is likely to occur at an asset within a given period of time in the future, wherein the historical operating data comprises (i) historical abnormal-condition data associated with one or more failures that occurred at the plurality of assets in the past and (ii) historical sensor data indicating operating conditions of the plurality of assets in the past;

receive sensor data indicating at least one operating condition of a given asset;

based on the received sensor data, execute the predictive model to determine, for the given asset, a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future; and

transmit, to a computing device, health-metric data indicating the determined health metric for the given asset to facilitate causing the computing device to display a representation of the determined health metric for the given asset.

14. The non-transitory computer-readable medium of claim 11, wherein the instructions are further executable to cause the computing system to:

determine that the health metric for the given asset is below a threshold value; and

based on determining that the health metric for the given asset is below a threshold value, cause an action to facilitate increasing the health metric for the given asset.

15. The non-transitory computer-readable medium of claim 14, wherein causing the action comprises causing the computing device to output an alert indicating a decrease in the health metric for the given asset.

16. The non-transitory computer-readable medium of claim 14, wherein causing the action comprises causing the computing device to output an indication of one or more recommended repairs to the given asset.

17. The non-transitory computer-readable medium of claim 14, wherein causing the action comprises causing the given asset to modify one or more operating conditions.

18. The non-transitory computer-readable medium of claim 11, wherein the health metric for the given asset comprises one of (i) a probability that no failure from the group of failures will occur at the given asset within the given period of time in the future or (ii) a probability that at least one failure from the group of failures will occur at the asset within the given period of time in the future.

12. (canceled)

13. (canceled)

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

based at least on historical operating data for a plurality of assets, defining a predictive model for outputting a health metric indicating whether at least one failure from a group of failures is likely to occur at an asset within a given period of time in the future, wherein the historical operating data comprises (i) historical abnormal-condition data associated with one or more failures that occurred at the plurality of assets in the past and (ii) historical sensor data indicating operating conditions of the plurality of assets in the past;

receiving sensor data indicating at least one operating condition of a given asset;

based on the received sensor data, executing the predictive model to determine, for the given asset, a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future; and

transmitting, to a computing device, health-metric data indicating the determined health metric for the given asset to facilitate causing the computing device to display a representation of the determined health metric for the given asset.

20. The computer-implemented method of claim 19, wherein the health metric for the given asset comprises one of (i) a probability that no failure from the group of failures will occur at the given asset within the given period of time in the future or (ii) a probability that at least one failure from the group of failures will occur at the given asset within the given period of time in the future.

21. The computer-implemented method of claim 19, further comprising:

determining that the health metric for the given asset is below a threshold value; and

based on determining that the health metric for the given asset is below a threshold value, causing an action to facilitate increasing the health metric for the given asset.

22. The computer-implemented method of claim 21, wherein causing the action comprises causing the computing device to output an alert indicating a decrease in the health metric for the given asset.

23. The computer-implemented method of claim 21, wherein causing the action comprises causing the computing device to output an indication of one or more recommended repairs to the given asset.

24. The computer-implemented method of claim 21, wherein causing the action comprises causing the given asset to modify one or more operating conditions.