Remaining Useful Lifetime Prediction for the Equipment with the Random Failure Threshold

Abstract

Prognostics and health management (PHM) technology is widely used in industrial production, and its core is to predict the remaining useful life (RUL) of the equipment. For the existing research of RUL prediction, the impact of random failure threshold (RFT) has not been analyzed. To solve this problem, an RUL prediction method based on the Kalman filter is proposed. Firstly, a nonlinear Wiener degradation model is built in this paper. Then, the parameters of the degradation model are estimated by the maximum likelihood estimation (MLE) method and the distribution coefficients of RFT are calculated by the expected maximum (EM) algorithm. In addition, the Kalman filtering technique is applied to renewal the degradation states by obtaining condition monitoring (CM) data. Finally, the analytical expression of probability density function (PDF) for the RUL is derived by considering the RFT. The simulation example shows that the method in this paper has advantages of RUL prediction, and thus can have potentially engineering application value.