Train Wheel Degradation Modeling and Remaining Useful Life Prediction Based on Mixed Effect Model Considering Dependent Measurement Errors

Abstract

In order to predict the remaining useful life of rail train wheels progressively under dependent measurement errors, an extended mixed effect degradation model and Bayesian parameter update framework is proposed. The heteroscedastic and correlated structure of error terms are considered in the modeling process, and the spectral decomposition of the error effect matrix is used to realize the independent conversion of online monitoring data. Based on the Bayesian method, the online monitoring data is merged with historical knowledge for parameter updating, and remaining useful life prediction and reliability evaluation are performed. The case study results show that the proposed method considering dependent measurement errors could provide adequate degradation modeling and improve the accuracy of remaining useful life prediction. These results will help the development of prognostic and health management of train wheel.