# US 2016/0154690 A1 · Subsystem Health Score

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- Number: US 2016/0154690 A1
- Document ID: US20160154690A1
- Status: Published application
- Kind: A1
- Application number: 14/732,285
- Filed: 2015-06-05
- Published: 2016-06-02
- Earliest claimed: 2014-12-01
- Assignees: Uptake, LLC
- Applicants: Uptake, LLC
- Inventors: Michael Horrell; John Ciasulli; Sheng Zhong; Adam McElhinney; Brian Silva
- Web page: https://patentgrep.com/patent/US20160154690A1

## CPC

- 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)}
- G01D 3/08: with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
- 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)}
- G01M 99/008: {by doing functionality tests}
- 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 program data in numerical form (G05B19/418 takes precedence)
- 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}
- G05B 23/0254: {based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks}
- G05B 23/0275: {Fault isolation and identification, e.g. classify fault; estimate cause or root of failure}
- G06F 11/008: {Reliability or availability analysis}
- G06F 11/0709: {in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems}
- G06F 11/0721: {within a central processing unit [CPU]}
- G06F 11/0751: {Error or fault detection not based on redundancy (power supply failures G06F1/30; network fault management H04L41/06)}
- G06F 11/0754: {by exceeding limits}
- G06F 11/0772: {Means for error signaling, e.g. using interrupts, exception flags, dedicated error registers}
- G06F 11/0787: {Storage of error reports, e.g. persistent data storage, storage using memory protection}
- 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)}
- G06F 11/2007: {using redundant communication media}
- G06F 11/26: Functional testing
- 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}
- G06N 5/02: Knowledge representation; Symbolic representation
- G06N 5/04: Inference or reasoning models
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- 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)
- G06Q 10/06312: {Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling}
- G06Q 10/0633: Workflow analysis
- G06Q 10/06334: {using forecasting or predicting}
- G06Q 10/067: Enterprise or organisation modelling
- G06Q 10/20: Administration of product repair or maintenance
- G06Q 50/04: Manufacturing
- G06Q 50/08: Construction
- G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)}
- G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)}
- G07C 5/0825: {using optical means}
- G08B 21/18: Status alarms (G08B21/02 takes precedence)
- G06F 2201/85: Active fault masking without idle spares
- G16H 40/40: for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
- Y02P 90/30: Computing systems specially adapted for manufacturing
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G01D: MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## 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

### Claim 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:
identify at least one abnormal-condition indicator associated with a given subsystem of an asset;
based on the identified at least one abnormal-condition indicator, identify a subset of historical operating data for a plurality of assets, 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;
based on the identified subset of the historical operating data, 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 the given subsystem within a given period of time in the future;
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 subsystem 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.

### Claim 2 (depends on 1)

The computing system of claim 1, wherein identifying the at least one abnormal-condition indicator associated with the given subsystem comprises:
identifying one or more sensors associated with the given subsystem; and
identifying one or more abnormal-condition indicators corresponding to the one or more sensors, wherein the one or more abnormal-condition indicators comprise the at least one abnormal-condition indicator.

### Claim 3 (depends on 2)

The computing system of claim 2, wherein identifying the one or more sensors associated with the given subsystem comprises identifying the one or more sensors associated with the given subsystem based on the historical operating data and at least one of historical repair data or sensor attributes.

### Claim 4 (depends on 1)

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

### Claim 5 (depends on 1)

The computing system of claim 1, wherein the at least one abnormal-condition indicator comprises a plurality of abnormal-condition indicators, and wherein each failure from the group of failures corresponds to at least one abnormal-condition indicator from the plurality of abnormal-condition indicators.

### Claim 6 (depends on 5)

The computing system of claim 5, wherein the predictive model for outputting the health metric comprises a predictive model for outputting a probability that any of the plurality of abnormal-condition indicators will be triggered within the given period of time in the future.

### Claim 7 (depends on 1)

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:
based on the determined health metric for the given asset, cause an action to facilitate modifying the health metric for the given asset.

### Claim 8 (depends on 7)

The computing system of claim 7, wherein causing the action comprises transmitting to a parts-ordering system part-order, data to facilitate causing the parts-ordering system to order a component of the given subsystem for the given asset.

### Claim 9 (depends on 7)

The computing system of claim 7, wherein causing the action comprises causing the given asset to modify an operating condition of the given subsystem.

### Claim 10 (depends on 1)

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 subsystem 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 subsystem within the given period of time in the future.

### Claim 11

A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing system to:
identify at least one abnormal-condition indicator associated with a given subsystem of an asset;
based on the identified at least one abnormal-condition indicator, identify a subset of historical operating data for a plurality of assets, 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;
based on the identified subset of the historical operating data, 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 the given subsystem within a given period of time in the future;
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 subsystem 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.

### Claim 12 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein identifying the at least one abnormal-condition indicator associated with the given subsystem comprises:
identifying one or more sensors associated with the given subsystem; and
identifying one or more abnormal-condition indicators corresponding to the one or more sensors, wherein the one or more abnormal-condition indicators comprise the at least one abnormal-condition indicator.

### Claim 13 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein identifying the one or more sensors associated with the given subsystem comprises identifying the one or more sensors associated with the given subsystem based on the historical operating data and at least one of historical repair data or sensor attributes.

### Claim 14 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the at least one abnormal-condition indicator comprises a plurality of abnormal-condition indicators, and wherein each failure from the group of failures corresponds to at least one abnormal-condition indicator from the plurality of abnormal-condition indicators.

### Claim 15 (depends on 14)

The non-transitory computer-readable medium of claim 14, wherein the predictive model for outputting the health metric comprises a predictive model for outputting a probability that any of the plurality of abnormal-condition indicators will be triggered within the given period of time in the future.

### Claim 16 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the program instructions are further executable to cause the computing system to:
based on the determined health metric for the given asset, cause an action to facilitate modifying the health metric for the given asset.

### Claim 17 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein causing the action comprises causing the given asset to modify an operating condition of the given subsystem.

### Claim 18

A computer-implemented method, the method comprising:
identifying at least one abnormal-condition indicator associated with a given subsystem of an asset;
based on the identified at least one abnormal-condition indicator, identifying a subset of historical operating data for a plurality of assets, 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;
based on the identified subset of the historical operating data, 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 the given subsystem within a given period of time in the future;
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 subsystem 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.

### Claim 19 (depends on 18)

The computer-implemented method of claim 18, wherein identifying the at least one abnormal-condition indicator associated with the given subsystem comprises:
identifying one or more sensors associated with the given subsystem; and
identifying one or more abnormal-condition indicators corresponding to the one or more sensors, wherein the one or more abnormal-condition indicators comprise the at least one abnormal-condition indicator.

### Claim 20 (depends on 19)

The computer-implemented method of claim 19, wherein identifying the one or more sensors associated with the given subsystem comprises determining the one or more sensors associated with the given subsystem based on the historical operating data and at least one of historical repair data or sensor attributes.