System and method for maintenance planning and failure prediction for equipment subject to periodic failure risk

• US 9,058,568 B2
• Filed: 12/11/2012
• Issued: 06/16/2015
• Est. Priority Date: 12/11/2012
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Hosking, Jonathan R., Kalagnanam, Jayant R., Zhu, Yada
• Primary Examiner(s): JONES, HUGH M
• Application Number:
  US13/710,920
• Publication Number:
  US 20140163935A1
• Time in Patent Office: 917 Days
• Field of Search: 703/2
• US Class Current: 1/1
• 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 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

Systems and methods for failure prediction and maintenance planning are provided. A system for failure prediction and maintenance planning, comprises a statistical modeling module comprising a periodic impact evaluation module capable of identifying periodic effects on the failure risk, a balance equation systems module capable of constructing balance equations with respect to phases of failure times, and an initial phase estimation module capable of estimating an unknown initial phase, wherein one or more of the modules are implemented on a computer system comprising a memory and at least one processor coupled to the memory.

Claims

1. A method for failure prediction for equipment subject to periodically varying failure risk, the method comprising:

receiving collected prior failure data of the equipment in connection with at least one of an environmental condition and an operating condition, wherein the collected prior failure data was collected during a time period excluding a portion of a failure history of the equipment;

identifying periodic effects on the failure risk corresponding to the time period excluding the portion of the failure history of the equipment and modeling periodic patterns;

modeling failure times with the periodic patterns;

constructing balance equations with respect to phase values of the failure times, wherein;

the phase values represent a phase at respective failure times during a time period that the equipment is in operation; and

the phase values are elements of a set corresponding to a duration of a cycle;

estimating, based on the balance equations, an unknown initial phase value at a start of the time period excluding the portion of the failure history of the equipment; and

using the estimated initial phase value when predicting a subsequent failure time distribution of the equipment;

wherein the balance equations are constructed according to the following equation;

2. The method of claim 1, wherein the periodic patterns are modeled using a Fourier series.

3. The method of claim 1, wherein modeling the failure times comprises modeling a hazard rate with the periodic patterns, wherein the estimated initial phase value is used when modeling the hazard rate.

4. The method of claim 1, further comprising constructing a measure of discrepancy between actual and modeled failure times.

5. The method of claim 4, further comprising estimating parameters of the periodic patterns by evaluating the measure of discrepancy.

6. The method of claim 5, further comprising predicting reliability of the equipment.

7. The method of claim 6, further comprising:

evaluating reliability of parts from at least one of a plurality parts suppliers and a plurality of parts models;

optimizing selection of at least one of the plurality of parts suppliers and the plurality of parts models; and

ordering the parts based on at least one of said evaluating and said optimizing.

8. The method of claim 6, further comprising:

determining short and long term spare parts demand; and

ordering the spare parts based on said determining.

9. The method of claim 1, wherein the periodic effects are identified based on a Fourier transformation of failure intensity.

10. The method of claim 1, wherein the periodic effects are due to at least one of the environmental or operational conditions collected during the time period.

11. The method of claim 10, wherein the unknown initial phase value is a phase value of a last failure time before the time period.

12. The method of claim 1, further comprising performing at least one of dynamic programming, stochastic programming, nonlinear programming, heuristics algorithm processing and Markov Chain Monte Carlo processing on the collected prior failure data and results of the modeling step.