A Novel Robust Dual Unscented Particle Filter Method for Remaining Useful Life Prediction of Rolling Bearings

It is still challenging to accurately predict the remaining useful life (RUL) of bearings with fluctuating degradation processes. To address this issue, this article proposes a novel robust dual unscented particle filter (DUPF) method for RUL prediction. First, a dual-stream unscented particle filter model is constructed to leverage the hidden degradation information at different time scales with different prediction models, which enhances model's capability to track various fluctuating degradation trends. Second, a comprehensive fusion strategy is designed to adaptively optimize the weights of double streams, in which the maximum failure probability of dynamic Bayesian (DB) is quantitatively evaluated to improve the reliability of the prediction results. The proposed method is tested using two datasets and compared with several state-of-the-art methods. The results show that the proposed method can improve prediction accuracy and is robust to fluctuations in degradation processes.