Remaining Useful Lifetime Estimation with Random Failure Threshold and Measurement Error

Abstract

Measurement error and random failure threshold widely exist in the real degradation process. Aiming at the current methods about the remaining useful lifetime (RUL) estimation without considering the influence of measurement error and random failure threshold, an approximate analytical RUL distribution in a closed-form of a linear Wiener based degradation process with measurement errors is proposed. The expectation maximization (EM) algorithm is used to estimate the unknown parameters in the model, and the Bayesian method is used to update the model parameters. Then, the probability density function (PDF) of RUL is derived based on the random failure threshold. The simulation results show that considering measurement errors and random failure threshold can significantly improve the accuracy of RUL estimation.