Remaining Useful Life Prediction of Nonlinear Wiener Process-Based Degradation Model Based on Multi Source Information with Considering Random Effects

Abstract

Accurate parameters estimation is the premise of accurate remaining useful life (RUL) prediction. This paper proposes a RUL prediction method of nonlinear Wiener process based on multi source information with considering random effects. First, a nonlinear Wiener process with considering random effects is used to model the degradation process of equipment. Then, according to the nature of parameters estimation, nonlinear parameter can be obtained based on historical degradation data. After that, the expectation maximization (EM) algorithm is used to calculate fixed parameter and random coefficient in model with fusing prior degradation information and prior failure time data information. Finally, fatigue crack data are used for experimental verification. Compared with the method based on historical degradation data or failure time data, the method based on multi source information with considering random effects can effectively improve the accuracy of parameters estimation and RUL estimation.