Aggregate predictive model and workflow for local execution

• US 10,261,850 B2
• Filed: 06/19/2015
• Issued: 04/16/2019
• Est. Priority Date: 12/01/2014
• Status: Active Grant

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake Technologies, Inc.
• Inventors: Nicholas, Brad, Kolb, Jason
• Primary Examiner(s): Nilsson, Eric
• Application Number:
  US14/744,352
• Publication Number:
  US 20160371599A1
• Time in Patent Office: 1,397 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 predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Claims

1. A computing system comprising:

a network interface configured to communicatively couple the computing system via a communication network to assets and local analytics devices located remote from the computing 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;

receive operating data for a plurality of assets, wherein the operating data comprises historical sensor data associated with past occurrences of a given type of failure event at the plurality of assets;

based at least on the received historical sensor data, define (i) a predictive model that is configured to predict occurrences of the given type of failure event and (ii) a corresponding workflow that is configured to run based on an output of the predictive model; and

transmit via the communication network, to at least one particular local analytics device that corresponds to and is located at or near a particular asset, the predictive model and the corresponding workflow and thereby configure the particular local analytics device to (i) locally apply the predictive model to sensor data received from the particular asset without involvement of the computing system and thereby predict occurrences of the given type of failure event and (ii) in response to a predicted occurrence of the given type of failure event, locally execute the corresponding workflow without involvement of the computing system such that one or more operations are initiated at the particular asset.

2. The computing system of claim 1, wherein the operating data further comprises historical abnormal-condition data associated with a failure that occurred at a given asset at a particular time, and wherein the historical sensor data indicates at least one operating condition of the given asset at the particular time.

3. The computing system of claim 1, wherein the predictive model is defined to output a probability that the given type of failure event will occur at a given asset within a period of time into the future.

4. The computing system of claim 3, wherein the corresponding workflow comprises one or more operations to be performed based on the determined probability.

5. The computing system of claim 1, wherein the corresponding workflow comprises a local analytics device corresponding to an asset triggering the asset to control one or more actuators of the asset to facilitate modifying an operating condition of the asset.

6. The computing system of claim 1, wherein the corresponding workflow comprises a local analytics device corresponding to an asset causing one or more diagnostic tools to be executed locally by the asset.

7. The computing system of claim 1, wherein the corresponding workflow comprises a local analytics device corresponding to an asset acquiring sensor data from the asset according to a particular data-acquisition scheme.

8. The computing system of claim 7, wherein the particular data-acquisition scheme indicates one or more sensors of the asset from which data is to be acquired.

9. The computing system of claim 8, wherein the particular data-acquisition scheme further indicates an amount of data that the local analytics device corresponding to the asset is to acquire from each of the one or more sensors of the asset.

10. The computing system of claim 1, wherein the corresponding workflow comprises a local analytics device corresponding to an asset transmitting data related to operation of the asset to the computing system according to a particular data-transmission scheme.

11. The computing system of claim 10, wherein the particular data-transmission scheme indicates a frequency at which the local analytics device corresponding to the asset is to transmit the data related to operation of the asset to the computing system.

12. The computing system of claim 1, wherein the computing system is a first computing system, and wherein the corresponding workflow comprises a local analytics device corresponding to an asset transmitting instructions to a second computing system to facilitate causing the second computing system to carry out an operation related to the asset.

13. The computing system of claim 1, wherein the particular local analytics device that corresponds to the particular asset comprises a first local analytics device that corresponds to a first asset, and wherein transmitting the predictive model and the corresponding workflow to the at least one particular local analytics device comprises transmitting the predictive model and the corresponding workflow to both the first local analytics device and also a second local analytics device that corresponds to and is located at or near a second asset and thereby (a) configuring the first local analytics device to (i) locally apply the predictive model to sensor data received from the first asset without involvement of the computing system and thereby predict occurrences of the given type of failure event at the first asset and (ii) in response to a predicted occurrence of the given type of failure event at the first asset, locally execute the corresponding workflow without involvement of the computing system such that one or more operations are initiated at the first asset, and (b) configuring the second local analytics device to (i) locally apply the predictive model to sensor data received from the second asset without involvement of the computing system and thereby predict occurrences of the given type of failure event at the second asset and (ii) in response to a predicted occurrence of the given type of failure event at the second asset, locally execute the corresponding workflow without involvement of the computing system such that one or more operations are initiated at the second asset.

14. A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing system comprising a network interface configured to communicatively couple the computing system via a communication network to assets and local analytics devices located remote from the computing system to:

receive operating data for a plurality of assets, wherein the operating data comprises historical sensor data associated with past occurrences of a given type of failure event at the plurality of assets;

based at least on the received historical sensor data, define (i) a predictive model that is configured to predict occurrences of the given type of failure event and (ii) a corresponding workflow that is configured to run based on an output of the predictive model; and

transmit via the communication network, to at least one particular local analytics device that corresponds to and is located at or near a particular asset, the predictive model and the corresponding workflow and thereby configure the particular local analytics device to (i) locally apply the predictive model to sensor data received from the particular asset without involvement of the computing system and thereby predict occurrences of the given type of failure event and (ii) in response to a predicted occurrence of the given type of failure event, locally execute the corresponding workflow without involvement of the computing system such that one or more operations are initiated at the particular asset.

15. The non-transitory computer-readable medium of claim 14, wherein the predictive model is defined to output a probability that the given type of failure event will occur at a given asset within a period of time into the future.

16. The non-transitory computer-readable medium of claim 14, wherein the corresponding workflow comprises a local analytics device corresponding to an asset triggering the asset to control one or more actuators of the asset to facilitate modifying an operating condition of the asset.

17. The non-transitory computer-readable medium of claim 14, wherein the corresponding workflow comprises a local analytics device corresponding to an asset causing one or more diagnostic tools to be executed locally by the asset.

18. The non-transitory computer-readable medium of claim 14, wherein the computing system is a first computing system, and wherein the corresponding workflow comprises a local analytics device corresponding to an asset transmitting instructions to a second computing system to facilitate causing the second computing system to carry out an operation related to the asset.

19. A computer-implemented method performed by a computing system configured to be communicatively coupled via a communication network to assets and local analytics devices located remote from the computing system, the method comprising:

receiving operating data for a plurality of assets, wherein the operating data comprises historical sensor data associated with past occurrences of a given type of failure event at the plurality of assets;

based at least on the received historical sensor data, defining (i) a predictive model that is configured to predict occurrences of the given type of failure event and (ii) a corresponding workflow that is configured to run based on an output of the predictive model; and

transmitting via the communication network, to at least one particular local analytics device that corresponds to and is located at or near a particular asset, the predictive model and the corresponding workflow and thereby configuring the particular local analytics device to (i) locally apply the predictive model to sensor data received from the particular asset without involvement of the computing system and thereby predict occurrences of the given type of failure event and (ii) in response to a predicted occurrence of the given type of failure event, locally execute the corresponding workflow without involvement of the computing system such that one or more operations are initiated at the particular asset.

20. The computer-implemented method of claim 19, wherein the corresponding workflow comprises a local analytics device corresponding to an asset acquiring sensor data from the asset according to a particular data-acquisition scheme, and wherein the particular data-acquisition scheme indicates one or more sensors of the asset from which data is to be acquired.