Adaptive handling of operating data based on assets' external conditions

• US 9,864,665 B2
• Filed: 09/06/2016
• Issued: 01/09/2018
• Est. Priority Date: 12/01/2014
• Status: Active Grant

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake Technologies, Inc.
• Inventors: McElhinney, Adam, Ciasulli, John, Horrell, Michael
• Primary Examiner(s): TRIEU, VAN THANH
• Application Number:
  US15/257,276
• Publication Number:
  US 20160378585A1
• Time in Patent Office: 490 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

Examples of systems, devices, and methods related to assets and asset operating conditions involve determining 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 operating conditions that normally result in triggering of 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;

receive (i) historical response data indicating a plurality of past responses to instances of a given abnormal-condition indicator for one or more assets, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset, and (ii) historical conditions data indicating values of at least one given condition impacting asset operation at the time of the respective occurrences of the given abnormal condition at the one or more assets;

based on the historical response data and the historical conditions data, identify a relationship between responses to the given abnormal-condition indicator and values of the at least one given condition impacting asset operation;

receive, for a given asset, (i) operating data indicating a given occurrence of the given abnormal condition at the given asset, wherein the operating data is associated with a given instance of the given abnormal-condition indicator, and (ii) conditions data indicating a value of the at least one given condition at the time of the given occurrence of the given abnormal condition at the given asset; and

based on (i) the received operating data and (ii) the received conditions data, handle the given instance of the given abnormal-condition indicator in accordance with the determined relationship.

2. The computing system of claim 1, wherein the determined relationship indicates that the given abnormal-condition indicator is ignored or disregarded when a value of the at least one given condition satisfies given criteria.

3. The computing system of claim 2, wherein handling the given instance of the given abnormal-condition indicator in accordance with the determined relationship comprises causing the given instance of the given abnormal-condition indicator to be suppressed if the value of the at least one given condition satisfies the given criteria.

4. The computing system of claim 1, wherein the at least one given condition comprises an ambient temperature of an asset.

5. The computing system of claim 1, wherein the at least one given condition comprises a location of an asset.

6. The computing system of claim 5, wherein handling, based on (i) the received operating data and (ii) the received conditions data, the given instance of the given abnormal-condition indicator in accordance with the determined relationship comprises:

based on the received conditions data, determining that the location of the given asset at the time of the given occurrence of the given abnormal condition is within a threshold proximity of one or more locations that have a correlation to a given type of response to instances of the given abnormal-condition indicator; and

based at least on the determination, handling the given instance of the given abnormal-condition indicator in accordance with the given type of response.

7. The computing system of claim 6, wherein the conditions data is first conditions data, 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 system to:

after handling the given instance of the given abnormal-condition indicator in accordance with the determined relationship, determine based on second conditions data that the location of the given asset is no longer within a threshold proximity of the one or more locations; and

based at least on the determination, cease handling the given instance of the given abnormal-condition indicator in accordance with the given type of response.

8. The computing system of claim 1, wherein the operating data indicating the given occurrence of the given abnormal condition comprises the given instance of the abnormal-condition indicator.

9. The computing system of claim 1, wherein the operating data indicating the given occurrence of the given abnormal condition comprises sensor data that causes the computing system to generate the given instance of the abnormal-condition indicator.

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

receive (i) historical response data indicating a plurality of past responses to instances of a given abnormal-condition indicator for one or more assets, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset, and (ii) historical conditions data indicating values of at least one given condition impacting asset operation at the time of the respective occurrences of the given abnormal condition at the one or more assets;

based on the historical response data and the historical conditions data, identify a relationship between responses to the given abnormal-condition indicator and values of the at least one given condition impacting asset operation;

receive, for a given asset, (i) operating data indicating a given occurrence of the given abnormal condition at the given asset, wherein the operating data is associated with a given instance of the given abnormal-condition indicator, and (ii) conditions data indicating a value of the at least one given condition at the time of the given occurrence of the given abnormal condition at the given asset; and

based on (i) the received operating data and (ii) the received conditions data, handle the given instance of the given abnormal-condition indicator in accordance with the determined relationship.

11. The non-transitory computer-readable medium of claim 10, wherein the determined relationship indicates that the given abnormal-condition indicator is ignored or disregarded when a value of the at least one given condition satisfies given criteria.

12. The non-transitory computer-readable medium of claim 11, wherein handling the given instance of the given abnormal-condition indicator in accordance with the determined relationship comprises causing the given instance of the given abnormal-condition indicator to be suppressed if the value of the at least one given condition satisfies the given criteria.

13. The non-transitory computer-readable medium of claim 10, wherein the at least one given condition comprises an ambient temperature of an asset.

14. The non-transitory computer-readable medium of claim 10, wherein the at least one given condition comprises a location of an asset.

15. The non-transitory computer-readable medium of claim 14, wherein handling, based on (i) the received operating data and (ii) the received conditions data, the given instance of the given abnormal-condition indicator in accordance with the determined relationship comprises:

based on the received conditions data, determining that the location of the given asset at the time of the given occurrence of the given abnormal condition is within a threshold proximity of one or more locations that have a correlation to a given type of response to instances of the given abnormal-condition indicator; and

based at least on the determination, handling the given instance of the given abnormal-condition indicator in accordance with the given type of response.

16. The non-transitory computer-readable medium of claim 15, wherein the conditions data is first conditions data, and wherein the instructions are further executable to cause the computing system to:

after handling the given instance of the given abnormal-condition indicator in accordance with the determined relationship, determine based on second conditions data that the location of the given asset is no longer within a threshold proximity of the one or more locations; and

based at least on the determination, cease handling the given instance of the given abnormal-condition indicator in accordance with the given type of response.

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

receiving (i) historical response data indicating a plurality of past responses to instances of a given abnormal-condition indicator for one or more assets, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset, and (ii) historical conditions data indicating values of at least one given condition impacting asset operation at the time of the respective occurrences of the given abnormal condition at the one or more assets;

based on the historical response data and the historical conditions data, identifying a relationship between responses to the given abnormal-condition indicator and values of the at least one given condition impacting asset operation;

receiving, for a given asset, (i) operating data indicating a given occurrence of the given abnormal condition at the given asset, wherein the operating data is associated with a given instance of the given abnormal-condition indicator, and (ii) conditions data indicating a value of the at least one given condition at the time of the given occurrence of the given abnormal condition at the given asset; and

based on (i) the received operating data and (ii) the received conditions data, handling the given instance of the given abnormal-condition indicator in accordance with the determined relationship.

18. The computer-implemented method of claim 17, wherein the determined relationship indicates that the given abnormal-condition indicator is ignored or disregarded when a value of the at least one given condition satisfies given criteria, and wherein handling the given instance of the given abnormal-condition indicator in accordance with the determined relationship comprises causing the given instance of the given abnormal-condition indicator to be suppressed if the value of the at least one given condition satisfies the given criteria.

19. The computer-implemented method of claim 17, wherein the at least one given condition comprises a location of an asset.

20. The computer-implemented method of claim 19, wherein handling, based on (i) the received operating data and (ii) the received conditions data, the given instance of the given abnormal-condition indicator in accordance with the determined relationship comprises:

based on the received conditions data, determining that the location of the given asset at the time of the given occurrence of the given abnormal condition is within a threshold proximity of one or more locations that have a correlation to a given type of response to instances of the given abnormal-condition indicator; and

based at least on the determination, handling the given instance of the given abnormal-condition indicator in accordance with the given type of response.