A Bayesian change point detection-based method for rolling bearing remaining useful life prediction

Addressing the multi-stage characteristics of rolling bearing degradation with random change points, this paper proposes a novel method for predicting the Remaining Useful Life (RUL) of multi-stage degradation processes. Initially, the prior parameters of each stage model are estimated using offline historical data. Then, for a single online device, real-time change point detection is performed using the Bayesian change point detection method. The Bayesian updating approach is adopted to update the parameters of the first stage before the change point occurs and the second stage after the change point. Subsequently, the multi-stage model is utilized for RUL prediction. Numerical simulations and case studies have shown that the rolling bearing life prediction method based on Bayesian change point detection can improve change point detection accuracy by 85%, thereby achieving high-precision multi-stage RUL prediction.