RUL Prediction of Adaptive Kernel Particle Filtering Based on Time-Varying Fading Factor Considering Dynamic External Environment

Dynamic environmental factors often introduce uncertainty into the degradation process of equipment, complicating remaining useful life (RUL) prediction. To address this challenge, the Wiener degradation process considering the impact of external dynamic environments is modeled, and a dynamic system filtering method based on a Bayesian framework is proposed for online RUL prediction. First, the Wiener process is constructed, accounting for the effects of dynamic environmental factors on the degradation rate and the correlation between the degradation rate and volatility. Then, to solve the uncertainty problem in the filtering process, a time-varying fading factor is introduced in the dynamic filtering process to adjust particle states in real time and improve the ability to track state changes. The nonparametric adaptive kernel density estimation (AKDE) is employed to approximate the true posterior probability distribution, and the discrete particle samples are converted into continuous probability density functions (PDFs), which increases the diversity of particles and optimizes the resampling strategy. Finally, the feasibility and effectiveness of the proposed model and algorithm are validated through a simulation study and the application of a lithium-ion battery.