Computer Architecture and Method for Modifying Intake Data Rate Based on a Predictive Model

• US 20170161130A1
• Filed: 12/08/2015
• Published: 06/08/2017
• Est. Priority Date: 12/01/2014
• Status: Active Grant

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake Technologies, Inc.
• Inventors: Goldstein, Michael, Ravensberg, Tom, Hansmann, Will
• Granted Patent: US 10,025,653 B2
• Time in Patent Office: 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 is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.

Claims

1. A computing system comprising:

a data intake system;

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;

operate in a first mode in which the computing system ingests operating data received from a given asset of a plurality of assets at a first ingestion rate;

while operating in the first mode, (a) receive operating data from the given asset, (b) ingest at least a portion of the received operating data at the first ingestion rate, and (c) based on at least a portion of the ingested data, execute a predictive model that outputs an indicator of whether at least one event from a group of events is likely to occur at the given asset within a given period of time in the future;

make a determination that the health metric satisfies a threshold condition;

in response to the determination, transition from operating in the first mode to operating in a second mode in which the computing system ingests operating data from the given asset at a second ingestion rate that differs from the first ingestion rate; and

while operating in the second mode, (a) receive operating data from the given asset and (b) ingest at least a portion of the received operating data at the second ingestion rate.

2. The computing system of claim 1, wherein the received operating data comprises one or both of (i) abnormal-condition data associated with a failure that occurred at the given asset at a particular time and (ii) sensor data indicating at least one operating condition of the given asset at the particular time.

3. The computing system of claim 1, wherein the group of events comprises a group of one or more failures, and wherein the predictive model outputs a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future.

4. The computing system of claim 3, wherein the predictive model is defined based on historical operating data comprising (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.

5. The computing system of claim 1, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises a probability that no event from the group of events is likely to occur at the given asset within the given period of time in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value.

6. The computing system of claim 1, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises a probability that no event from the group of events is likely to occur at the given asset within the given period of time in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value.

7. The computing system of claim 1, wherein the second ingestion rate is higher than the first ingestion rate.

8. The computing system of claim 1, wherein the second ingestion rate comprises a variable rate that is determined based on the indicator.

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

operate in a first mode in which the computing system ingests operating data received from a given asset of a plurality of assets at a first ingestion rate;

while operating in the first mode, (a) receive operating data from the given asset, (b) ingest at least a portion of the received operating data at the first ingestion rate, and (c) based on at least a portion of the ingested data, execute a predictive model that outputs an indicator of whether at least one event from a group of events is likely to occur at the given asset within a given period of time in the future;

make a determination that the health metric has reached a threshold; and

in response to the determination, (a) receive operating data from the given asset and (b) ingest at least a portion of the received operating data at the second ingestion rate.

10. The non-transitory computer-readable medium of claim 9, wherein the received operating data comprises one or both of (i) abnormal-condition data associated with a failure that occurred at the plurality of assets at a particular time and (ii) sensor data indicating at least one operating condition of the plurality of assets at the particular time.

11. The non-transitory computer-readable medium of claim 9, wherein the group of events comprises a group of one or more failures, and wherein the predictive model outputs a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future.

12. The non-transitory computer-readable medium of claim 11, wherein the predictive model is defined based on historical operating data comprising (i) historical abnormal-condition data associated with one or more failures that occurred at the given asset in the past and (ii) historical sensor data indicating operating conditions of the given asset in the past.

13. The non-transitory computer-readable medium of claim 9, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises a probability that no event from the group of events is likely to occur at the given asset within the given period of time in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value.

14. The non-transitory computer-readable medium of claim 9, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises a probability that no event from the group of events is likely to occur at the given asset within the given period of time in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value.

15. A computer-implemented method comprising:

a computing system operating in a first mode in which the computing system ingests operating data received from a given asset of a plurality of assets at a first ingestion rate;

while operating in the first mode, the computing system (a) receiving operating data from the given asset, (b) ingesting at least a portion of the received operating data at the first ingestion rate, and (c) based on at least a portion of the ingested data, executing a predictive model that outputs an indicator of whether at least one event from a group of events is likely to occur at the given asset within a given period of time in the future;

the computing system making a determination that the health metric has reached a threshold;

in response to the determination, the computing system transitioning from operating in the first mode to operating in a second mode in which the computing system ingests operating data from the given asset at a second ingestion rate that differs from the first ingestion rate; and

while operating in the second mode, the computing system (a) receiving operating data from the given asset and (b) ingesting at least a portion of the received operating data at the second ingestion rate.

16. The computer-implemented method of claim 15, wherein the received operating data comprises one or both of (i) abnormal-condition data associated with a failure that occurred at the given asset at a particular time and (ii) sensor data indicating at least one operating condition of the given asset at the particular time.

17. The computer-implemented method of claim 15, wherein the group of events comprises a group of one or more failures, and wherein the predictive model outputs a health metric indicating whether at least one failure from the group of failures is likely to occur at the given asset within the given period of time in the future.

18. The computer-implemented method of claim 17, wherein the predictive model is defined based on historical operating data comprising (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.

19. The computer-implemented method of claim 15, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises a probability that no event from the group of events is likely to occur at the given asset within the given period of time in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value.

20. The computer-implemented method of claim 15, wherein the indicator of whether at least one event from the group of events is likely to occur at the given asset within the given period of time in the future comprises a probability that no event from the group of events is likely to occur at the given asset within the given period of time in the future, and wherein the determination that the indicator satisfies the threshold condition comprises a determination that the probability is at or below a threshold value.