Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter

Rolling bearings are the key components of rotating machinery. Thus, the prediction of remaining useful life (RUL) is vital in condition-based maintenance (CBM). This paper proposes a new method for RUL prediction of bearings based on time-varying Kalman filter, which can automatically match different degradation stages of bearings and effectively realize the prediction of RUL. The evolution of monitoring data in normal and slow degradation stages is a linear trend, and the evolution in accelerated degradation stage is nonlinear. Therefore, Kalman filter models based on linear and quadratic functions are established. Meanwhile, a sliding window relative error is constructed to adaptively judge the bearing degradation stages. It can automatically switch filter models to process monitoring data at different stages. Then, the RUL can be predicted effectively. Two groups of bearing run-to-failure data sets are utilized to demonstrate the feasibility and validity of the proposed method.