Abstract
This paper presents a new general method for evaluating the reliability function and the mean residual life of degrading systems subject to condition monitoring and random failure. In the proposed method, the degradation process of the system is characterized by a continuous-time Markov chain, which is then incorporated into the proportional hazards model as a stochastic covariate process to describe the hazard rate of the time to system failure. Unlike the conventional method based on conditioning, which is applicable only for a small number of degradation states, the proposed method is capable of tackling the case with a general number of degradation states. Using the developed approximation techniques, closed-form formulas for related reliability characteristics are obtained in terms of the appropriate transition probability matrix. The proposed evaluation algorithm is computationally efficient and embeddable to support real-time reliability assessment of the system subject to condition monitoring for developing the optimal maintenance policy. The effectiveness and the accuracy of the method are validated by a numerical study and compared with the conventional method. A general case where the degradation path can be discretized up to ten states is also studied to illustrate the appealing general features.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Reliability |
| Volume | 67 |
| Issue number | 1 |
| Pages (from-to) | 13-25 |
| Number of pages | 13 |
| ISSN | 0018-9529 |
| DOIs | |
| Publication status | Published - Mar 2018 |
| Externally published | Yes |
Bibliographical note
Funding Information:Manuscript received November 28, 2016; revised July 5, 2017 and October 11, 2017; accepted November 24, 2017. Date of publication December 25, 2017; date of current version March 1, 2018. This work was supported in part by the Aeronautical Science Foundation of China under grant 20155553039, in part by the China Scholarship Council, and in part by the Natural Sciences and Engineering Research Council of Canada under grant RGPIN 121384-11. Associate Editor: E. Pohl. (Corresponding author: Viliam Makis.) S. Zhao is with the School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China, and also with the Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (e-mail: shuaiz@mie.utoronto.ca).
Publisher Copyright:
© 2018 IEEE.
Keywords
- Condition-based maintenance (CBM)
- continuous-time Markov chain (CTMC)
- prognostics and health management (PHM)
- proportional hazards model
- residual life prediction
- whole life-cycle transition probability matrix