Method and system for forecasting reliability of assets

• US 20070061232A1
• Filed: 08/31/2005
• Published: 03/15/2007
• Est. Priority Date: 08/31/2005
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Varma, Anil, Bonissone, Piero, Aggour, Kareem
• Granted Patent: US 7,509,235 B2
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A method and system of forecasting reliability of an asset is provided. The method includes identifying peer units of the asset by using selected criteria, performing a search for the peer units based upon the selected criteria, and constructing local predictive models using the peer units. The method also includes estimating the future behavior of the asset based upon the local predictive models and dynamically updating the local predictive models to reflect at least one change in the criteria.

Claims

1. A method of forecasting reliability of an asset, comprising:

identifying peer units of the asset by using selected criteria and performing a search for the peer units based upon the selected criteria;

constructing local predictive models using the peer units;

estimating the future behavior of the asset based upon the local predictive models; and

dynamically updating the local predictive models to reflect at least one change in the criteria.

2. The method of claim 1, wherein the selected criteria includes:

attributes;

search parameters; and

similarity function.

3. The method of claim 2, wherein the attributes include structural features and characteristics of the asset.

4. The method of claim 2, wherein the search parameters include a spread and slope of a distribution of attribute values returned from the search.

5. The method of claim 2, wherein the similarity function includes a defined degree of closeness between the asset and potential peer units, the defined degree of closeness including applying weights to the selected attributes for refining computation of the similarity function.

6. The method of claim 2, wherein the attributes include at least one of:

operational data relating to the asset;

maintenance data relating to the asset; and

environmental data relating to the asset.

7. The method of claim 1, wherein the dynamically updating the local predictive models to reflect at least one change in the criteria is performed using evolutionary search.

8. The method of claim 7, wherein the evolutionary search is performed by defining an evolutionary algorithm for tuning the selected criteria.

9. The method of claim 8, wherein the evolutionary algorithm is defined using at least one of a:

filter; and

wrapper.

10. The method of claim 1, wherein the dynamically updating the local predictive models is performed for selected time frames.

11. A system for forecasting reliability of an asset, comprising:

a host system;

a storage device in communication with the host system, the storage device storing asset data; and

an asset integrity engine executing on the host system, performing;

identifying peer units of the asset by using selected criteria and performing a search of the storage device for the peer units based upon the selected criteria;

constructing local predictive models using the peer units;

estimating the future behavior of the asset based upon the local predictive models; and

dynamically updating the local predictive models to reflect at least one change in the criteria.

12. The system of claim 11 wherein the selected criteria includes:

attributes;

search parameters; and

similarity function.

13. The system of claim 12, wherein the attributes include structural features and characteristics of the asset.

14. The system of claim 12, wherein the search parameters include a spread and slope of a distribution of attribute values returned from the search.

15. The system of claim 12, wherein the similarity function includes a defined degree of closeness between the asset and potential peer units, the defined degree of closeness including applying weights to the selected attributes for refining computation of the similarity function.

16. The system of claim 12, wherein the attributes include at least one of:

operational data relating to the asset;

maintenance data relating to the asset; and

environmental data relating to the asset.

17. The system of claim 11, wherein the dynamically updating the local predictive models to reflect at least one change in the criteria is performed using evolutionary search.

18. The system of claim 17, wherein the evolutionary search is performed by defining an evolutionary algorithm for tuning the selected criteria.

19. The system of claim 18, wherein the evolutionary algorithm is defined using at least one of a:

filter; and

wrapper.

20. The system of claim 11, wherein the dynamically updating the local predictive models is performed for selected time frames.