Adaptive handling of abnormal-condition indicator criteria

• US 9,910,751 B2
• Filed: 09/06/2016
• Issued: 03/06/2018
• 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:
  US15/257,258
• Publication Number:
  US 20160379465A1
• Time in Patent Office: 546 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 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;

maintain criteria that governs whether a given abnormal-condition indicator is generated, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal-condition at an asset;

receive historical response data indicating a plurality of past responses to displayed instances of the given abnormal-condition indicator;

based on the historical response data, modify the criteria that governs whether the given abnormal-condition indicator is generated; and

transmit the modified criteria to at least one asset for use in monitoring asset operation.

2. The computing system of claim 1, wherein the criteria that governs whether the given abnormal-condition indicator is generated comprises a sensor identifier and a first sensor value criteria, and wherein the modified criteria comprises the sensor identifier and a second sensor value criteria that differs from the first sensor value criteria.

3. The computing system of claim 2, wherein the first sensor value criteria comprises a first sensor value threshold, and wherein the second sensor value criteria comprises a second sensor value threshold that is greater than the first sensor value threshold.

4. The computing system of claim 1, wherein the criteria that governs whether the given abnormal-condition indicator is generated comprises a first set of sensor identifiers, and wherein the modified criteria comprises a second set of sensor identifiers that differs from the first set of sensor identifiers by at least one sensor identifier.

5. The computing system of claim 1, wherein modifying the criteria that governs whether the given abnormal-condition indicator is generated comprises:

based on the historical response data, determining that an extent of past responses comprising a decision to disregard a displayed instance of the given abnormal-condition indicator has reached a threshold extent; and

in response to the determination, modifying the criteria that governs whether the given abnormal-condition indicator is generated.

6. The computing system of claim 5, 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:

before modifying the criteria that governs whether the given abnormal-condition indicator is generated, define the threshold extent based on one or more characteristics of the given abnormal-condition indicator.

7. The computing system of claim 6, wherein the one or more characteristics of the given abnormal-condition indicator comprises one or more of (i) a severity level of the given abnormal condition indicated by the given abnormal-condition indicator or (ii) an asset subsystem associated with the given abnormal-condition indicator.

8. The computing system of claim 5, 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:

before receiving the historical response data indicating a plurality of past responses to displayed instances of the given abnormal-condition indicator, (i) receive instances of the given abnormal-condition indicator from the at least one asset, and (ii) cause one or more output systems to display the received instances of the given abnormal-condition indicator.

9. The computing system 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 system to:

before maintaining the criteria that governs whether a given abnormal-condition indicator is generated, define the criteria that governs whether a given abnormal-condition indicator is generated.

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

maintain criteria that governs whether a given abnormal-condition indicator is generated, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset;

receive historical response data indicating a plurality of past responses to displayed instances of the given abnormal-condition indicator;

based on the historical response data, modify the criteria that governs whether the given abnormal-condition indicator is generated; and

transmit the modified criteria to at least one asset for use in monitoring asset operation.

11. The non-transitory computer-readable medium of claim 10, wherein modifying the criteria that governs whether the given abnormal-condition indicator is generated comprises:

based on the historical response data, determining that an extent of past responses comprising a decision to disregard a displayed instance of the given abnormal-condition indicator has reached a threshold extent; and

in response to the determination, modifying the criteria that governs whether the given abnormal-condition indicator.

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

before modifying the criteria that governs whether the given abnormal-condition indicator is generated, define the threshold extent based on one or more characteristics of the given abnormal-condition indicator.

13. The non-transitory computer-readable medium of claim 12, wherein the one or more characteristics of the given abnormal-condition indicator comprises one or more of (i) a severity level of the given abnormal-condition indicated by the given abnormal-condition indicator or (ii) an asset subsystem associated with the given abnormal-condition indicator.

14. The non-transitory computer-readable medium of claim 10, wherein the instructions are further executable to cause the computing system to:

before receiving the historical response data indicating a plurality of past responses to displayed instances of the given abnormal-condition indicator, (i) receive instances of the given abnormal-condition indicator from the at least one asset, and (ii) cause one or more output systems to display the received instances of the given abnormal-condition indicator.

15. The non-transitory computer-readable medium of claim 10, wherein the instructions are further executable to cause the computing system to:

before maintaining the criteria that governs whether a given abnormal-condition indicator is generated, define the criteria that governs whether a given abnormal-condition indicator is generated.

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

maintaining criteria that governs whether a given abnormal-condition indicator is generated, wherein the given abnormal-condition indicator indicates an occurrence of a given abnormal condition at an asset;

receiving historical response data indicating a plurality of past responses to displayed instances of the given abnormal-condition indicator;

based on the historical response data, modifying the criteria that governs whether the given abnormal-condition indicator is generated; and

transmitting the modified criteria to at least one asset for use in monitoring asset operation.

17. The computer-implemented method of claim 16, wherein modifying the criteria that governs whether the given abnormal-condition indicator is generated comprises:

based on the historical response data, determining that an extent of past responses comprising a decision to disregard a displayed instance of the given abnormal-condition indicator has reached a threshold extent; and

in response to the determination, modifying the criteria that governs whether the given abnormal-condition indicator.

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

before modifying the criteria that governs whether the given abnormal-condition indicator is generated, defining the threshold extent based on one or more characteristics of the given abnormal-condition indicator.

19. The computer-implemented method of claim 18, wherein the one or more characteristics of the given abnormal-condition indicator comprises one or more of (i) a severity level of the given abnormal-condition indicated by the given abnormal-condition indicator or (ii) an asset subsystem associated with the given abnormal-condition indicator.

20. The computer-implemented method of claim 16, the method further comprising:

before receiving the historical response data indicating a plurality of past responses to displayed instances of the given abnormal-condition indicator, (i) receive instances of the given abnormal-condition indicator from the at least one asset, and (ii) cause one or more output systems to display the received instances of the given abnormal-condition indicator.