An Adaptive Levy Process Model for Remaining Useful Life Prediction

Predicting remaining useful life (RUL) is a crucial part of prognostics and health management (PHM), which has attracted widespread attention in academia and industry over the past few decades. Effective estimation of RUL is predicated on the development of a suitable degradation model for the system. However, most of the existing models require offline learning of a priori parameters, which is not applicable in the absence of historical data. To solve this problem, a stochastic degradation model based on the Levy process is proposed in this study. The utilization of the Kalman filter and expectation-maximization in conjunction with Rauch-Tung-Striebel (KF-EM-RTS) enables the acquisition and real-time updating of model parameters and the probability density function (pdf) for RUL. This approach also allows for the characterization of parameter uncertainty. Simultaneously, the analytical expression of the RUL pdf can be obtained by the definition based on first hitting time (FHT) and Levy-Khinchin formula. The high-speed computer numerical control (CNC) milling machine cutters dataset from the 2010 IEEE Data Challenge and solid-state RF power amplifier (SSRFPA) degradation data are utilized to validate the proposed model. The results of the experiments demonstrate that the proposed model is capable of delivering accurate RUL estimations.