Background:
Remaining useful life (RUL) estimation is the central mission to the complex
systems’ prognostics and health management. During 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 briefly 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, the possible future trends are concluded
tentatively.
Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines
