A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability

Abstract

Abstract The estimation of remaining useful life (RUL) for a degrading system has gained increasing attention both in academia and in industry for decades. In this study, a nonlinear-drift-driven Wiener process model considering three common sources of uncertainty is constructed by an age-dependent state-space model for the RUL estimation of degrading systems. Analytical expressions approximating the probability distribution function of RUL of the above-described model are derived for both online estimation and offline estimation scenarios. It is shown that the derived expressions are more general and cover the simplified cases discussed in previous woks. A model parameter estimation method is proposed based on unbalanced historical degradation measurements, and a down-sampling strategy is introduced to avert the underflow issue. The prognostic performance of the proposed method against previous similar works under the online and offline estimation scenarios is demonstrated on two publicly available datasets by comparisons in terms of three prognostic metrics and the probabilistic distribution of RUL at different condition monitoring points. The results show that it is necessary to include the nonlinear degradation characteristics and the three sources of uncertainty into the RUL estimation especially for the offline estimation scenario.