Remaining Useful Life Prediction for Degradation Processes With Dependent and Nonstationary Increments

Remaining useful life (RUL) prediction is critical for health management of industrial equipment. It has been widely noted that degradation modeling is a core step for RUL prediction where the Brownian motion (BM)-based models attract much attention. However, the existing BM-based degradation models still have some impractical assumptions, where the increments of a BM are independent and stationary. To extend the application of the degradation models, a bifractional Brownian motion (biFBM)-based degradation model is developed in this article. The biFBM is a process with dependent and nonstationary increments, which includes the BM and fractional Brownian motion (FBM) as special cases. For the proposed degradation model, the estimation of parameters and degradation states as well as the prediction of RUL is further considered. To address the non-Markovian degradation processes, an improved particle filter is designed for degradation state estimation and RUL prediction. The proposed degradation model and RUL prediction method are validated by case studies of turbine engines and a blast furnace wall.