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.

Rolling bearing degradation trend prediction based on composite multiscale grey entropy and dynamic particle filter

“…The fusion method that combines physical model and datadriven can leverage the benefits of both approaches to obtain more accurate prediction results. Common fusion methods include the digital twin technology [9], the method that combines the exponential model with Kalman filtering [3], the method that combines exponential model with Wiener process [10], the method that combines the Paris model with particle filter (PF) [11], etc. Among them, PF based methods have been widely used in the field of prognosis [12].…”