Remaining useful life estimation based on a nonlinear Wiener process model with CSN random effects

Abstract

Remaining useful life (RUL) prediction of rolling bearings is crucial to equipment operation and maintenance. The data-driven Wiener-based methods have aroused widespread concern. The commonly adopted distribution to represent inter-product variation in the Wiener degradation model is the normal distribution. However, this assumption is not appropriate for some practical applications. Aiming at this problem, an adaptive nonlinear Wiener process model with degradation drift satisfying the closed skew-normal (CSN) distribution to model the degradation characteristic exhibiting non-normality and asymmetry in inter-product variation is proposed in this paper. An online recursive algorithm based on Bayes’ theorem is derived to update the hidden drift distribution. A Bayesian smoother based on CSN distribution is developed, which is employed to estimate model parameters involving the expectation–maximization algorithm. Furthermore, an analytical expression of the RUL distribution is derived. Finally, a numerical case and a practical bearing dataset are provided to illustrate the proposed method.