Asset health score

• US 10,417,076 B2
• Filed: 06/05/2015
• Issued: 09/17/2019
• Est. Priority Date: 12/01/2014
• Status: Active Grant

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake Technologies, Inc.
• Inventors: Ciasulli, John, Horrell, Michael, Zhong, Sheng, Kolb, Jason
• Primary Examiner(s): Nghiem, Michael P
• Application Number:
  US14/732,258
• Publication Number:
  US 20160153806A1
• Time in Patent Office: 1,565 Days
• Field of Search: 702184
• 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:

a network interface configured to facilitate communication with a plurality of assets equipped with sensors and a plurality of computing devices;

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;

for each respective failure type of a group of failure types;

(i) identify one or more types of abnormal-condition indicators associated with a respective failure type,

(ii) identify past instances of the identified one or more types of abnormal-condition indicators at the plurality of assets equipped with sensors, wherein each past instance of a respective type of abnormal-condition indicator is indicative of a past occurrence of a respective type of abnormal condition at one asset of the plurality of assets,

(iii) identify historical sensor data corresponding to the identified past instances of the identified one or more types of abnormal-condition indicators, wherein the historical sensor data for each past instance of a respective type of abnormal-condition indicator indicates operating conditions of the one asset associated with a past occurrence of a respective type of abnormal condition at the one asset, and

(iv) apply a supervised machine learning technique to the identified historical sensor data to define a respective predictive model for the respective failure type that is configured to receive sensor data for the one asset as input and

output a value indicating whether the respective failure type is likely to occur at the one asset within a given period of time in the future;

combine the respective predictive models for the group of failure types into an aggregated predictive model that is configured to receive sensor data for the one asset as input and output a health metric indicating whether at least one failure type from the group of failure types is likely to occur at the one asset within a given period of time in the future;

receive sensor data indicating operating conditions of a given asset;

apply the aggregated predictive model to the received sensor data to determine, for the given asset, a health metric indicating whether at least one failure type from the group of failure types is likely to occur at the given asset within the given period of time in the future;

detect that the health metric for the given asset satisfies threshold criteria; and

in response to the detection, cause a computing device to display a visual indicator that at least one failure type from the group of failure types is likely to occur at the given asset.

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

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

4. 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:

in response to the detection that the health metric for the given asset satisfies the threshold criteria, carry out a remedial action to facilitate increasing the operating health of the given asset.

5. The computing system of claim 4, wherein carrying out the remedial action comprises one or more of (a) automatically generating and sending, to a computing device associated with an individual responsible for overseeing the given asset, an alert indicating that the given asset is considered to be in a state of impending failure, (b) automatically generating and sending, to the given asset, an instruction for the given asset to modify its operation to account for the detection that the given asset is considered to be in a state of impending failure, (c) automatically generating and sending, to a repair facility, an instruction to repair the given asset, and (d) automatically generating and sending, to a parts-ordering system, an instruction for the parts ordering system to order a given component of the given asset.

6. The computing system of claim 5, wherein the alert indicating that the given asset is considered to be in a state of impending failure includes an indication of one or more recommended repairs to the given asset.

7. The computing system of claim 4, wherein the remedial action is one given remedial action from a plurality of remedial actions, and wherein the program instructions are further executable by the at least one processor to cause the computing system to:

after carrying out the given remedial action, determine feedback data associated with carrying out the given remedial action; and

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

8. The computing system of claim 1, wherein the group of failure types is defined based on user input.

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

for each respective failure type of a group of failure types;

(i) identify one or more types of abnormal-condition indicators associated with a respective failure type,

(ii) identify past instances of the identified one or more types of abnormal-condition indicators at a plurality of assets equipped with sensors, wherein each past instance of a respective type of abnormal-condition indicator is indicative of a past occurrence of a respective type of abnormal condition at one asset of the plurality of assets,

(iii) identify historical sensor data corresponding to the identified past instances of the identified one or more types of abnormal-condition indicators, wherein the historical sensor data for each past instance of a respective type of abnormal-condition indicator indicates operating conditions of the one asset associated with a past occurrence of a respective type of abnormal condition at the one asset, and

(iv) apply a supervised machine learning technique to the identified historical sensor data to define a respective predictive model for the respective failure type that is configured to receive sensor data for the one asset as input and output a value indicating whether the respective failure type is likely to occur at the one asset within a given period of time in the future;

combine the respective predictive models for the group of failure types into an aggregated predictive model that is configured to receive sensor data for the one asset as input and output a health metric indicating whether at least one failure type from the group of failure types is likely to occur at the one asset within a given period of time in the future;

receive sensor data indicating operating conditions of a given asset;

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

detect that the health metric for the given asset satisfies threshold criteria; and

in response to the detection, cause a computing device to display a visual indicator that at least one failure type from the group of failure types is likely to occur at the given asset.

10. The non-transitory computer-readable medium of claim 9, wherein the instructions are further executable to cause the computing system to:

in response to the detection that the health metric for the given asset satisfies the threshold criteria, carry out a remedial action to facilitate increasing the operating health of the given asset.

11. The non-transitory computer-readable medium of claim 10, wherein carrying out the remedial action comprises one or more of (a) automatically generating and sending, to a computing device associated with an individual responsible for overseeing the given asset, an alert indicating that the given asset is considered to be in a state of impending failure, (b) automatically generating and sending, to the given asset, an instruction for the given asset to modify its operation to account for the detection that the given asset is considered to be in a state of impending failure, (c) automatically generating and sending, to a repair facility, an instruction to repair the given asset, and (d) automatically generating and sending, to a parts-ordering system, an instruction for the parts ordering system to order a given component of the given asset.

12. The non-transitory computer-readable medium of claim 11, wherein the alert indicating that the given asset is considered to be in a state of impending failure includes an indication of one or more recommended repairs to the given asset.

13. The non-transitory computer-readable medium of claim 9, wherein the health metric for the given asset comprises one of (i) a probability that no failure type from the group of failure types will occur at the given asset within the given period of time in the future or (ii) a probability that at least one failure type from the group of failure types will occur at the given asset within the given period of time in the future.

14. The non-transitory computer-readable medium of claim 9, wherein the group of failure types is defined based on user input.

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

for each respective failure type of a group of failure types;

(i) identifying one or more types of abnormal-condition indicators associated with a respective failure type,

(ii) identifying past instances of the identified one or more types of abnormal-condition indicators at a given set of assets equipped with sensors, wherein each past instance of a respective type of abnormal-condition indicator is indicative of a past occurrence of a respective type of abnormal condition at one asset of the plurality of assets,

(iii) identifying historical sensor data corresponding to the identified past instances of the identified one or more types of abnormal-condition indicators at the plurality of assets, wherein the historical sensor data for each past instance of a respective type of abnormal-condition indicator indicates operating conditions of the one asset associated with a past occurrence of a respective type of abnormal condition at the one asset, and

(iv) applying a supervised machine learning technique to the identified historical sensor data to define a respective predictive model for the respective failure type that is configured to receive sensor data for the one asset as input and output a value indicating whether the respective failure type is likely to occur at the one asset within a given period of time in the future;

combining the respective predictive models for the group of failure types into an aggregated predictive model that is configured to receive sensor data for the one asset as input and output a health metric indicating whether at least one failure type from the group of failure types is likely to occur at the one asset within a given period of time in the future;

receiving sensor data indicating operating conditions of a given asset;

applying the aggregated predictive model to the received sensor data to determine, for the given asset, a health metric indicating whether at least one failure type from the group of failure types is likely to occur at the given asset within the given period of time in the future;

detecting that the health metric for the given asset satisfies threshold criteria; and

in response to the detecting, causing a computing device to display a visual indicator that at least one failure type from the group of failure types is likely to occur at the given asset.

16. The computer-implemented method of claim 15, wherein the health metric for the given asset comprises one of (i) a probability that no failure type from the group of failure types will occur at the given asset within the given period of time in the future or (ii) a probability that at least one failure type from the group of failure types will occur at the given asset within the given period of time in the future.

17. The computer-implemented method of claim 15, further comprising:

in response to detecting that the health metric for the given asset satisfies the threshold criteria, carry out a remedial action to facilitate increasing the operating health of the given asset.

18. The computer-implemented method of claim 17, wherein carrying out the remedial action comprises one or more of (a) automatically generating and sending, to a computing device associated with an individual responsible for overseeing the given asset, an alert indicating that the given asset is considered to be in a state of impending failure, (b) automatically generating and sending, to the given asset, an instruction for the given asset to modify its operation to account for the detection that the given asset is considered to be in a state of impending failure, (c) automatically generating and sending, to a repair facility, an instruction to repair the given asset, and (d) automatically generating and sending, to a parts-ordering system, an instruction for the parts ordering system to order a given component of the given asset.

19. The computer-implemented method of claim 18, the alert indicating that the given asset is considered to be in a state of impending failure includes an indication of one or more recommended repairs to the given asset.

20. The computer-implemented method of claim 15, wherein the group of failure types is defined based on user input.