Prognostics for Linear Stochastic Degrading Systems With Survival Measurements

This paper addresses the problem of how to estimate the remaining useful life (RUL) for partially observed linear stochastic degrading systems with survival measurements. The motivation of this paper arises from two engineering facts-the measured degradation signals are taken from a survival degradation path and the degradation progression of the system is inevitably affected by multisource variability. To do so, we first revisit the linear degradation modeling framework driven by the Wiener process to account for the above two facts, and a new state-space model with non-Gaussian state transitions is constructed based on the constraint of survival measurements. Then, the particle filtering algorithm is applied to real-time estimate the non-Gaussian degradation state and random-effect parameter from measurements. Furthermore, we derive the probabilistic distribution of the RUL which can be real-time updated based on the available measurements. To apply the presented method, a two-stage estimation procedure based on particle expectation maximization algorithm is presented to determine and update the model parameters. The novelty of the presented method is to exclude the probability of failure before the current monitoring time and account for both impacts of the multisource variability and surviving degradation path on the RUL estimation. Finally, we demonstrate the proposed approach by a case study on gyros in the inertial platform.