Remaining useful life estimation: A review on stochastic process-based approaches

被引:0
|
作者
Du D. [1 ]
Zhang J. [1 ]
Si X. [1 ]
Hu C. [1 ]
机构
[1] Department of Automation, Xi’an Research Institute of High-Technology, Xi’an
基金
中国国家自然科学基金;
关键词
Condition-based maintenance; Degradation modeling; Prognostics and health management; Reliability; Remaining useful life; Stochastic process models;
D O I
10.2174/1872212114999200423115526
中图分类号
学科分类号
摘要
Background: Remaining Useful Life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During the last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a brief review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, possible future trends are concluded tentatively. © 2021 Bentham Science Publishers.
引用
收藏
页码:69 / 76
页数:7
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