Online Estimation of Degradation State Under Random Change of Mode

Abstract

Due to the changes in the surrounding environment, the dynamic of one degradation process may change at a random time, and it follows different modes before and after the change point. For solving online degradation state estimation problems subject to random change of mode, a novel state estimation method is proposed in this article based on the degradation models and the related monitored data. The proposed method employs the sequential probability ratio test (SPRT) based on the log-likelihood ratio to detect the unknown change time of the degradation mode and particle filtering to estimate the degradation states given observations and also evaluate the decision functions of the SPRT. Two case studies referring to a pneumatic valve considering single- and multiple-change times of the degradation mode are presented to illustrate the accuracy and effectiveness of the proposed method.