MULTI-STAGE FAILURE ANALYSIS AND PREDICTION

• US 20140281713A1
• Filed: 03/14/2013
• Published: 09/18/2014
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Hampapur, Arun, Li, Hongfei, Li, Zhiguo, Zhu, Yada
• Granted Patent: US 9,262,255 B2
• Time in Patent Office: Days
• US Class Current: 714/26
• 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 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• 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: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

A hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets is built. The hierarchical multi-stage model considers heterogeneity of failure patterns for the assets. At least one data stream is analyzed to determine whether the hierarchical multi-stage model needs to be updated due to a change in the failure patterns. If the analysis indicates that the hierarchical multi-stage model needs to be updated, the hierarchical multi-stage model is dynamically updated to obtain an updated hierarchical multi-stage model.

Claims

1. A method comprising:

building a hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets, said hierarchical multi-stage model considering heterogeneity of failure patterns for said assets;

analyzing at least one data stream to determine whether said hierarchical multi-stage model needs to be updated due to a change in said failure patterns; and

if said analysis indicates that said hierarchical multi-stage model needs to be updated, dynamically updating said hierarchical multi-stage model to obtain an updated hierarchical multi-stage model.

2. The method of claim 1, wherein said building comprises:

building a failure frequency analysis engine of said hierarchical multi-stage model;

building a failure association analysis engine of said hierarchical multi-stage model; and

building a failure prediction analysis engine of said hierarchical multi-stage model.

3. The method of claim 1, wherein in said building step, said considering of said heterogeneity of failure patterns for said assets comprises considering seasonality and geographical distribution.

4. The method of claim 1, wherein said analyzing of said data stream comprises analyzing at least one of equipment failure history, maintenance records, equipment attributes, and sensor data.

5. The method of claim 1, wherein said analyzing of said data stream comprises carrying out statistical model-based change detection.

6. The method of claim 1, wherein said analyzing of said data stream comprises carrying out data mining model-based change detection.

7. The method of claim 1, wherein said dynamic updating of said hierarchical multi-stage model comprises applying a Bayesian inference technique.

8. The method of claim 7, further comprising using said hierarchical multi-stage model as an input to said Bayesian inference technique.

9. The method of claim 1, further comprising providing a system, wherein the system comprises distinct software modules, each of the distinct software modules being embodied on a computer-readable storage medium, and wherein the distinct software modules comprise a hierarchical modeling module, a change detection module, and a model updating module;

wherein;

said building is carried out by said hierarchical modeling module executing on at least one hardware processor;

said analyzing is carried out by said change detection module executing on said at least one hardware processor; and

said dynamic updating is carried out by said model updating module executing on said at least one hardware processor.

10. An apparatus comprising:

a memory;

at least one processor, coupled to said memory, and;

a non-transitory tangible, computer-readable, recordable storage medium having embodied thereon instructions which when loaded into said memory configure said at least one processor to implement;

a hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets, said hierarchical multi-stage model considering heterogeneity of failure patterns for said assets, said hierarchical multi-stage model in turn comprising;

a failure frequency analysis engine;

a failure association analysis engine; and

a failure prediction analysis engine;

a change detection component which analyzes at least one data stream to determine whether said hierarchical multi-stage model needs to be updated due to a change in said failure patterns; and

a sequential model updating component which, if said analysis indicates that said hierarchical multi-stage model needs to be updated, dynamically updates said hierarchical multi-stage model to obtain an updated hierarchical multi-stage model.

11. The apparatus of claim 10, wherein said hierarchical multi-stage model considers said heterogeneity of said failure patterns for said assets by considering seasonality and geographical distribution.

12. The apparatus of claim 10, wherein said change detection component analyzes said data stream by analyzing at least one of equipment failure history, maintenance records, equipment attributes, and sensor data.

13. The apparatus of claim 10, wherein said change detection component analyzes said data stream by carrying out statistical model-based change detection.

14. The apparatus of claim 10, wherein said change detection component analyzes said data stream by carrying out data mining model-based change detection.

15. The apparatus of claim 10, wherein said sequential model updating component applies a Bayesian inference technique.

16. The apparatus of claim 15, wherein said sequential model updating component uses said hierarchical multi-stage model as an input to said Bayesian inference technique.

17. A computer program product comprising a computer readable storage medium having computer readable program code embodied therewith, said computer readable program code comprising:

computer readable program code configured to build a hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets, said hierarchical multi-stage model considering heterogeneity of failure patterns for said assets;

computer readable program code configured to analyze at least one data stream to determine whether said hierarchical multi-stage model needs to be updated due to a change in said failure patterns; and

computer readable program code configured to, if said analysis indicates that said hierarchical multi-stage model needs to be updated, dynamically update said hierarchical multi-stage model to obtain an updated hierarchical multi-stage model.

18. The computer program product of claim 17, wherein said computer readable program code configured to build comprises:

computer readable program code configured to build a failure frequency analysis engine of said hierarchical multi-stage model;

computer readable program code configured to build a failure association analysis engine of said hierarchical multi-stage model; and

computer readable program code configured to build a failure prediction analysis engine of said hierarchical multi-stage model.

19. The computer program product of claim 17, wherein in said computer readable program code configured to build, said considering of said heterogeneity of failure patterns for said assets comprises considering seasonality and geographical distribution.

20. The computer program product of claim 17, wherein said computer readable program code configured to analyze said data stream comprises computer readable program code configured to analyze at least one of equipment failure history, maintenance records, equipment attributes, and sensor data.