A sliding sequence importance resample filtering method for rolling bearings remaining useful life prediction based on two Wiener-process models

Abstract The remaining useful life (RUL) prediction of rolling bearings is an important part of prognostic and health management of mechanical systems. The model based on Wiener process can describe the time variability in the degradation process of bearings. However, in practical engineering, the degradation trends of bearings are often inconsistent, and it is difficult to fit the actual degradation trends of bearings with a single Wiener process model-based filtering method. Therefore, to improve the generalization ability, this paper uses linear model and exponential model based on Wiener process to predict bearing RUL. A sliding sequence importance resample filtering algorithm is proposed to track the degradation state of the bearing and reduce the prediction error by combining the two degradation models. Last, the superiority and effectiveness of the proposed method are illustrated by comparing with other commonly used RUL prediction methods on the basis of PRONOSTIA dataset.