Two-phase degradation modeling and remaining useful life prediction using nonlinear wiener process

Remaining useful life (RUL) is an essential part of prognostic and health management, which can be employed to enhance the reliability and reduce the maintenance cost. For many products, due to the change of external operating conditions and internal mechanisms, their degradation trajectories tend to exhibit two-phase patterns. In the current method, linear Wiener process is used in each phase for degradation modeling and RUL prediction. In practice, the degradation process of each phase exhibits nonlinear characteristics, where using linear Wiener process to establish two-phase model is often inadequate. In this paper, a novel approach for two-phase degrading product is proposed. A degradation model using nonlinear Wiener process is adopted to characterize the two-phase degradation trajectory firstly. The maximum likelihood estimation (MLE) is used to estimate the unknown parameters of proposed model and Bayesian method is employed for updating the parameters. Taking into account the randomness of the initial state transition to the changing state and the variability in different units degradation, the approximate analytical solution of RUL under the concept of the first passage time is derived. Finally, the effectiveness of the proposed RUL prediction method is demonstrated through a simulation study and a turbofan engine degradation dataset.