A simulation-based remaining useful life prediction method considering the influence of maintenance activities

Abstract

As the key of the prevalent prognostics and health management, remaining useful life prediction has attracted considerable attentions during the past decades. However, almost all of the existing remaining useful life prediction methods were implemented under the premise that the deteriorating systems were not maintained over the whole life cycle. For the deteriorating systems experiencing maintenance activities during their life profiles, this paper presents a simulation-based remaining useful life prediction method taking the influence of maintenance activities into account. Specifically, the Wiener process with jumps is employed to model the degradation path of a deteriorating system, where the jump parts are used to characterize the influence of maintenance activities on the system degradation. The parameters in the degradation model are estimated by the maximum likelihood estimation method. To acquire the remaining useful life distributions of the deteriorating system, we design a simulation-based algorithm on the basis of the Markov Chain Monte Carlo method. Accordingly, the interested statistics associated with the remaining useful life can be obtained numerically. Finally, a numerical example is provided to show the implementation of the newly proposed remaining useful life prediction method.

A remaining useful life prediction method for airborne fuel pump after maintenance

“…The density function f ðt; , , l Þ for RUL can be obtained only by changing the failure threshold l in equation (14)…”

“…You and Meng 13 state that for a repairable system receiving imperfect maintenance, there is still some damage or degradation accumulated after the maintenance; they introduce a hazard rate increase factor and age reduction factor to capture the effect of the maintenance. Wang et al 14 consider the influence of maintenance and propose a simulation-based RUL prediction method; they also use a factor to characterize the accumulated influence of maintenance activities as well. But there is no theoretical direction for the determination of the factors, which are always given by empirical.…”

Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity

“…103 In addition, the system degrades until the next maintenance or failure whichever comes first. Recently, Wang et al 104 employed a renewal-reward process perturbed by a diffusion, which was also defined as a Wiener process with random jumps elsewhere, to model the influence of maintenances. The most inspiring idea in this work is that jumps with random arriving time and amplitude opposite to the trend of degradation are incorporated.…”

Remaining useful life prediction of equipment considering dynamic thresholds under the influence of maintenance

“…Wang and other scholars [27] stems from Wiener process, viewed the effect of maintenance on equipment degradation process as an effect on the degradation amount and used normal distribution to represent the variation of the degradation amount after maintenance. Reference [26] did not only rely on the normal distribution to depict the change in degradation resulting from maintenance but also delved into exploring the use of an exponential distribution to depict the amount of degradation wreaked by maintenance.…”