Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions

Mechanical components mainly work under time-varying operating conditions, causing considerable complexity and variability to degradation signals and making it difficult to estimate the remaining useful life (RUL). To handle this problem, this paper proposes a stochastic hybrid system approach to predict the RUL online under time-varying operating conditions by modelling the degradation signals and operating conditions of components. The discrete dynamic nature of operating conditions is described by a continuous-time Markov chain, and their influences on the degradation signals are quantified by degradation rates and signal jumps in a state-space model that is constructed based on nonlinear Wiener process considering the unit-to-unit variability. Considering that a component may have different failure time points under different operating conditions, a novel simulation method based on sequential Monte Carlo is proposed to simulate future operating profiles, predict multiple failure time points and avoid delayed alarms. Hence, the definition of the RUL of the component can be determined through task orientation according to different functional requirements of users, and the accuracy of RUL prediction results can be significantly improved with engineering application value. The effectiveness of the proposed approach is verified via a simulation study and an accelerated degradation test of machine tool spindles.

Automated summary

This paper proposes a stochastic hybrid system approach to predict the remaining useful life (RUL) online under time-varying operating conditions by modelling the degradation signals and operating conditions of components. The proposed approach improves the accuracy of RUL prediction results by considering the discrete dynamic nature of operating conditions and their influences on the degradation signals.