# US 2016/0155098 A1 · Historical Health Metrics

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- Number: US 2016/0155098 A1
- Document ID: US20160155098A1
- Status: Published application
- Kind: A1
- Application number: 14/732,303
- Filed: 2015-06-05
- Published: 2016-06-02
- Earliest claimed: 2014-12-01
- Assignees: Uptake, LLC
- Applicants: Uptake, LLC
- Inventors: Adam McElhinney
- Web page: https://patentgrep.com/patent/US20160155098A1

## 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:
receive operating data for a set of assets;
based on the received operating data, execute a predictive model to determine, for each asset in the set of assets, a respective health metric indicating whether at least one failure from a group of failures is likely to occur at the asset within a given period of time in the future;
monitor the respective health metric for each asset in the set of assets;
identify a subset of assets that includes any asset from the set of assets having a monitored health metric that satisfies a threshold condition;
based at least on asset-related data for the identified subset of assets, identify at least one influencing variable associated with the identified subset of assets;
based on the identified at least one influencing variable, generate at least one recommendation related to asset health; and
transmit to a computing device, recommendation data indicating the at least one recommendation to facilitate causing the computing device to display a representation of the at least one recommendation.

### Claim 2 (depends on 1)

The computing system of claim 1, wherein the at least one influencing variable comprises at least one of an asset variable, an operator variable, or a maintenance variable.

### Claim 3 (depends on 2)

The computing system of claim 2, wherein the at least one influencing variable comprises the asset variable, and wherein the asset variable comprises an asset class, an asset travel schedule, an asset payload, or an asset environment.

### Claim 4 (depends on 2)

The computing system of claim 2, wherein the at least one influencing variable comprises the operator variable, and wherein the operator variable comprises an operator identifier, an operator schedule, or an operator habit.

### Claim 5 (depends on 2)

The computing system of claim 2, wherein the at least one influencing variable comprises the maintenance variable, and wherein the maintenance variable comprises a location of a repair, a repair-shop identifier, or a mechanic identifier.

### Claim 6 (depends on 1)

The computing system of claim 1, wherein identifying the at least one influencing variable based at least on the asset-related data for the identified subset of assets comprises identifying the at least one influencing variable based on at least one of operating data or maintenance data.

### Claim 7

(canceled)

### Claim 8

(canceled)

### Claim 9 (depends on 1)

The computing system of claim 1, wherein the at least one influencing variable comprises a mechanic identifier identifying a mechanic that repairs assets, and wherein the at least one recommendation comprises a recommendation to use the identified mechanic.

### Claim 10 (depends on 1)

The computing system of claim 1, wherein the at least one influencing variable comprises a given asset class, and wherein the at least one recommendation comprises a recommendation to purchase assets of the given asset class.

### Claim 11 (depends on 1)

The computing system of claim 1, wherein the at least one influencing variable comprises an operator habit indicating a range of operating conditions, and wherein the program instructions are further executable by the at least one processor to cause the computing system to:
transmit to at least one asset of the identified set of assets a command to facilitate causing the at least one asset to operate according to the range of operating conditions.

### Claim 12

A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing system to:
receive operating data for a set of assets;
based on the received operating data, execute a predictive model to determine, for each asset in the set of assets, a respective health metric indicating whether at least one failure from a group of failures is likely to occur at the asset within a given period of time in the future;
monitor the respective health metric for each asset in the set of assets;
identify a subset of assets that includes any asset from the set of assets having a monitored health metric that satisfies a threshold condition;
based at least on asset-related data for the identified subset of assets, identify at least one influencing variable associated with the identified subset of assets;
based on the identified at least one influencing variable, generate at least one recommendation related to asset health; and
transmit to a computing device, recommendation data indicating the at least one recommendation to facilitate causing the computing device to display a representation of the at least one recommendation.

### Claim 13 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein identifying the at least one influencing variable based at least on the asset-related data for the identified subset of assets comprises identifying the at least one influencing variable based on at least one of operating data or maintenance data.

### Claim 14

(canceled)

### Claim 15

(canceled)

### Claim 16 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein the at least one influencing variable comprises a mechanic identifier identifying a mechanic that repairs assets, and wherein the at least one recommendation comprises a recommendation to use the identified mechanic.

### Claim 17 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein the at least one influencing variable comprises a given asset class, and wherein
the at least one recommendation comprises a recommendation to purchase assets of the given asset class.

### Claim 18

A computer-implemented method, the method comprising:
receiving operating data for a set of assets;
based on the received operating data, executing a predictive model to determine, for each asset in the set of assets, a respective health metric indicating whether at least one failure from a group of failures is likely to occur at the asset within a given period of time in the future;
monitoring the respective health metric for each asset in the set of assets;
identifying a subset of assets that includes any asset from the set of assets having a monitored health metric that satisfies a threshold condition;
based at least on asset-related data for the identified subset of assets, identifying at least one influencing variable associated with the identified subset of assets;
based on the identified at least one influencing variable, generating at least one recommendation related to asset health; and
transmitting, to a computing device, recommendation data indicating the at least one recommendation to facilitate causing the computing device to display a representation of the at least one recommendation.

### Claim 19

(canceled)

### Claim 20

(canceled)

### Claim 21 (depends on 1)

The computing system of claim 1, wherein the asset-related data for the identified subset of assets is identified based on the monitored health metrics for the identified subset of assets.

### Claim 22 (depends on 1)

The computing system of claim 1, wherein generating the at least one recommendation based on the identified at least one influencing variable comprises:
determining that a given asset in the set of assets does not satisfy threshold criteria for the at least one influencing variable; and
in response to the determining, generating a recommendation to improve the asset health of the given asset.

### Claim 23 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein the asset-related data for the identified subset of assets is identified based on the monitored health metrics for the identified subset of assets.

### Claim 24 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein generating the at least one recommendation based on the identified at least one influencing variable comprises:
determining that a given asset in the set of assets does not satisfy threshold criteria for the at least one influencing variable; and
in response to the determining, generating a recommendation to improve the asset health of the given asset.

### Claim 25 (depends on 18)

The computer-implemented method of claim 18, wherein the asset-related data for the identified subset of assets is identified based on the monitored health metrics for the identified subset of assets.

### Claim 26 (depends on 18)

The computer-implemented method of claim 18, wherein generating the at least one recommendation based on the identified at least one influencing variable comprises:
determining that a given asset in the set of assets does not satisfy threshold criteria for the at least one influencing variable; and
in response to the determining, generating a recommendation to improve the asset health of the given asset.