Title:Remaining Useful life Estimation: A Review on Stochastic Process-based Approaches
VOLUME: 15 ISSUE: 1
Author(s):Dangbo Du, Jianxun Zhang, Xiaosheng Si* and Changhua Hu
Affiliation:Department of Automation, Xi’an Research Institute of High-Technology, Xi’an, Department of Automation, Xi’an Research Institute of High-Technology, Xi’an, Department of Automation, Xi’an Research Institute of High-Technology, Xi’an, Department of Automation, Xi’an Research Institute of High-Technology, Xi’an
Keywords:Remaining useful life, degradation modeling, stochastic process models, reliability, prognostics and health management,
condition-based maintenance.
Abstract: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.
