Switching State-Space Degradation Model With Recursive Filter/Smoother for Prognostics of Remaining Useful Life

Abstract

Remaining useful life (RUL) is a critical metric in prognostics and health management (PHM) because it reflects the future health status and fault progression of products. Most RUL estimation methods are based on degradation data. In practice, due to changing degradation mechanisms during products’ whole life cycle, the degradation data may consist of two or more distinct phases, and the time points of these mechanisms switching are usually nondeterministic. This property makes RUL estimation a difficult task. To solve this problem, this paper proposes a switchable state-space degradation model to characterize degradation paths with nondeterministic switching manner dynamically. To update the model parameters by newly available data, a novel statistical procedure based on Rao-Blackwellized filter/smoother and an expectation maximization algorithm is derived. To improve the robustness and efficiency of the RUL prediction, a semianalytic prediction model is developed, which can avoid significant fluctuation in RUL estimation. The developed methodologies can automatically track different degradation phases and adaptively update parameters related to prior distributions. Two real products degradation cases are used to verify our methodologies.