System and method for predicting the timing of future service events of a product

• US 6,799,154 B1
• Filed: 05/25/2000
• Issued: 09/28/2004
• Est. Priority Date: 05/25/2000
• Status: Expired due to Term

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Stein, Jeffrey William, Aragones, James Kenneth
• Primary Examiner(s): Teska, Kevin J.
• Assistant Examiner(s): Thangavelu, Kandasamy
• Application Number:
  US09/578,095
• Time in Patent Office: 1,587 Days
• Field of Search: 703/7, 703/22, 703/13, 703/25, 701/10, 701/29, 701/19, 702/182, 702/34, 707/200, 705/3, 716/4
• US Class Current: 703/22
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A system and method for predicting timing of future service events of a product. A database contains a plurality of service information and performance information for the product. A statistical analyzer analyzes the plurality of processed service information to determine a plurality of compartment failure information. A performance deterioration rate analyzer analyzes the performance deterioration rate of the product from the plurality of service information and performance information. A simulator, simulates a distribution of future service events of the product according to the plurality of compartment failure information and the performance deterioration rate analysis.

Claims

1. A system for predicting the timing of a future service event of a product formed from a plurality of compartments, comprising:

a database that contains a plurality of service information and a plurality of performance information for the product;

a statistical analyzer that analyzes the plurality of service information to determine a plurality of compartment failure information comprising compartment failure variables and compartment time-to-failure coefficients, wherein the stastistical analyzer uses the plurality of compartment failure information to determine which compartment failure variables influence the timing of future service events and estimate time-to-failure distributions for the plurality of compartments;

a performance deterioration rate analyzer that analyzes performance deterioration rate of the product from the plurality of service information and performance information, wherein the performance deterioration rate analyzer comprises a statistical analysis script that relates performance information of a subset of compartments of the product according to time, wherein the statistical analysis script generates an estimated deterioration rate curve for the subset of compartments of the product, wherein the performance deterioration rate analyzer further comprises a transformer that transforms each estimated deterioration rate curve for a compartment to a performance life distribution; and

a simulator for simulating a distribution of future service events of the product according to the time-to-failure distributions and performance life distributions plurality.

2. The system according to claim 1, wherein the database comprises a service database and a performance historical database.

3. The system according to claim 1, wherein the plurality of performance information comprises compartment definitions, repair history and service factors.

4. The system according to claim 1, wherein the plurality of performance information comprises performance characteristic values, initial data levels after servicing, currant data levels, dales at which the product is serviced, and variables that affect the servicing of a subset of the plurality of compartments.

5. The system according in claim 1, further comprising a preprocessor for processing the plurality of service information into a predetermined format.

6. The system according to claim 5, wherein the preprocessor generates a plurality of data files according to the plurality of service information.

7. The system accosting to claim 1, wherein the statistical analyzer uses the estimated time-to-fallure distributions to determine a Weibull distribution for a subset of the plurality of compartments defined to the product.

8. The system according to claim 1, wherein the statistical analyzer comprises a service analysis script that executes a plurality of statistical procedures.

9. The system according to claim 8, wherein the plurality of statistical procedures comprise a multivariate regression and/or a correlation analysis.

10. The system according to claim 8, wherein the service analysis script generates a plurality of statistical diagnostic information.

11. The system according to claim 10, wherein the plurality of statistical diagnostic information comprises goodness-of-fit metrics and collinearity diagnostics.

12. The system according to claim 8, wherein the service analysis script generates a plurality of residual plots.

13. The system according to claim 1, wherein the statistical analyzer comprises a validation script.

14. The system according to claim 13, wherein the validation script is applied to a plurality of case studies set up for the product.

15. The system according to claim 1, wherein the simulator uses the performance life distributions to determine a Weibull distribution for a subset of the plurality of compartments defined for the product.

16. The system according to claim 1, wherein the simulator forecasts a service plan for the future service events that comprises the time for scheduling the service events.

17. A system for predicting the timing of a future service event of a product formed from a plurality of compartments, comprising:

means for containing a plurality of service information end a plurality of performance information for the product;

means for analyzing the plurality of service information to determine a plurality of compartment failure information comprising compartment failure variables and compartment time-to-failure coefficients, wherein the analyzing means uses the plurality of compartment failure information to determine which compartment failure variables influence the timing of future service events and estimate time-to-failure distributions for the compartments;

means for performing a deterioration rate analysis that determines performance deterioration rate of the product from the plurality of service information and performance information, wherein the performing means comprises a statistical analysis script that relates performance information of a subset of the plurality of compartments of the product according to time, wherein the statistical analysis script generates an estimated deterioration rate curve for a subset of the plurality of compartments of the product, wherein the performing means further comprises means for transforming each estimated deterioration rate curve for a compartment to a performance life distribution; and

means for simulating a distribution of future service events of the product according to the time-to-failure distributions and performance life distributions plurality of compartment.

18. The system according to claim 17, wherein the plurality of service information comprises compartment definitions, repair history and service factors.

19. The system according to claim 17, wherein the plurality of performance information comprises performance characteristic values, initial data levels after servicing, current data levels, dates at which the product is serviced, and variables that effect the servicing of a subset of the plurality of compartments of the product.

20. The system according to claim 17, further comprising means for preprocessing the plurality of service information into a predetermined format.

21. The system according to claim 17, wherein the preprocessing means generates a plurality of data files according to the plurality of service information.

22. The system according to claim 17, wherein the analyzing means uses the estimated time-to-failure distributions to determine a Weibull distribution for a subset of the plurality of compartments defined for the product.

23. The system according to claim 17, wherein the analyzing means comprises a service analysis script that executes a plurality of statistical procedures.

24. The system according to claim 23, wherein the plurality of statistical procedures comprise a multivariate regression and/or a correlation analysis.

25. The system according to claim 23, wherein the service analysis script generates a plurality of statistical diagnostic information.

26. The system according to claim 25, wherein the plurality of statistical diagnostic information comprises goodness-of-fit metrics and collinearity diagnostics.

27. The system according to claim 23, wherein the service analysis script generates a plurality of residual plots.

28. The system according to claim 17, wherein the analyzing means comprises a validation script.

29. The system according to claim 28, wherein the validation script is applied to a plurality of case studies set up for the product.

30. The system according to claim 17, wherein the simulator uses the performance life distribution to determine a Weibull distribution for a subset of the plurality of compartments defined for the product.

31. The system according to claim 17, wherein the simulator forecasts a service plan for the future service events that comprises the time for scheduling the service events.

32. A method for predicting the timing of a future service event of a product formed from a plurality of compartments, comprising;

storing a plurality of service information and a plurality of performance information for the product;

analyzing the plurality of service information to determine a plurality of compartment failure information comprising compartment failure variables and compartment time-to-failure coefficients, wherein the analyzing uses the plurality of compartment failure information to determine which compartment failure variables influence the timing of future service events and estimate time-to-failure distributions for the plurality of compartments;

performing a deterioration rate analysis of the product from the plurality of service information and performance information, wherein the performing comprises using a statistical analysis script that relates performance information of a subset of the plurality of compartments of the product according to time, wherein the statistical analysis script generates an estimated deterioration rate curve for a subset of the plurality of compartments of the product, wherein the performing a deterioration rate analysis further comprises transforming each estimated deterioration rate curve for a compartment to a performance life distribution; and

simulating a distribution of future service events of the product according to the time-to-failure distributions and performance life distributions.

33. The method according to claim 32, wherein the plurality of service information comprises compartment definitions, repair history and service factors.

34. The method according to claim 32, wherein the plurality of performance information comprises performance characteristic values, initial data levels after servicing, current data levels, dates at which the product is serviced, and variables that affect the servicing of a subset of the plurality of compartments of the product.

35. The method according to claim 32, further comprising preprocessing the plurality of service information into a predetermined format.

36. The method according to claim 35, wherein the preprocessing generates a plurality of data files according to the plurality of service information.

37. The method according to claim 32, wherein the analyzing uses the estimated time-to-failure distributions to determine a Weilbull distribution for a subset of the plurality of compartments.

38. The method according to claim 32, wherein the analyzing comprises using a service analysis script that executes a plurality of statistical procedures.

39. The method according to claim 38, a wherein the plurality of statistical procedures comprise a multivariate regression and/or a correlation analysis.

40. The method according to claim 39, wherein the service analysis script generates a plurality of statistical diagnostic information.

41. The method according to claim 40, wherein the plurality of statistical diagnostic intonation comprises goodness-of-fit metrics and collinearity diagnostics.

42. The method according to claim 38, wherein the service analysis script generating a plurality of residual plots.

43. The method according to claim 32 wherein the analyzing comprises using a validation script.

44. The method according to claim 43, wherein the validation script is applied to a plurality of case studies set up for the product.

45. The method according to claim 32, wherein the simulating uses the performance life distributions to determine a Weibull distribution for a subset of the plurality of compartments.

46. The method according to claim 32, wherein the simulating forecasts a service plan for the future service events that comprises the time for scheduling the service events.

47. A computer-readable medium storing computer instructions which when executed on a computer system predict the timing of a future service event of a product formed from a plurality of compartments, the computer instructions comprising:

storing a plurality of service information and a plurality of performance information for the product;

analyzing the plurality of service information to determine a plurality of compartment failure information comprising compartment failure variables and compartment time-to-failure coefficients, wherein the analyzing instructions uses the plurality of compartment failure information to determine which compartment failure variables influence the timing of future service events and estimates time-to-failure distributions for the plurality of compartments;

performing a deterioration rate analysis of the product from the plurality of service information and performance information, wherein the performing instructions comprise using a statistical analysis script that relates performance information of a subset of the plurality of compartments of the product according to time, wherein the statistical analysis script generates an estimated deterioration rate curve for a subset of the plurality of compartments of the product, wherein the performing instructions further comprise transforming instructions that transform each estimated deterioration rate curve to a performance life distribution; and

simulating a distribution of future service events of the product according to the time-to-failure distributions and performance life distributions.

48. The computer-readable medium according to claim 47, wherein the plurality of service information comprises compartment definitions, repair history and service factors.

49. The computer-readable medium according to claim 47, wherein the plurality of performance information comprises performance characteristic values, initial data levels after servicing, current data levels, dates at which the product is serviced, and variables that affect the servicing of a subset of the plurality of compartments of the product.

50. The computer-readable medium according to claim 47, further comprising preprocessing instructions that preprocess the plurality of service information into a predetermined format.

51. The computer-readable medium according to claim 50, wherein the preprocessing instructions generates a plurality of data files according to the plurality of service information.

52. The computer-readable medium according to claim 47, wherein the analyzing instructions use the estimated time-to-failure distributions to determine a Weibull distribution for a subset of the plurality of compartments.

53. The computer-readable medium according to claim 47, wherein the analyzing instructions comprises instructions for using a service analysis script that executes a plurality of statistical procedures.

54. The computer-readable medium according to claim 53, wherein the plurality of statistical procedures comprise a multivariate regression and/or a correlation analysis.

55. The computer-readable medium according to claim 54, wherein the service analysts script generates a plurality of statistical diagnostic information.

56. The computer-readable medium according to claim 55, wherein the plurality of statistical diagnostic information comprises goodness-of-fit metrics and collinearity diagnostics.

57. The computer-readable medium according to claim 53, wherein the service analysis script generates a plurality of residual plots.

58. The computer-readable medium according to claim 47, wherein the analyzing instructions comprise using a validation script.

59. The computer-readable medium according to claim 58, wherein the validation script is applied to a plurality of case studies set up for the product.

60. The computer-readable medium according to claim 47, wherein the simulating instructions use the performance life distribution to determine a Weibull distribution for a subset of the plurality of compartments.

61. The computer-readable medium according to claim 47, wherein the simulating instructions forecasts a service plan far the future service events that comprises the time for scheduling the service events.