Remaining useful life prediction for complex systems with multiple indicators of stochastic correlation considering random shocks

Abstract

The remaining useful life (RUL) prediction in complex systems is subject to complex working conditions, such as overloads, random vibration and shocks, gradual corrosion and wearing, that cause the complex and variable performance degradation of the mechanical system. Accurate prediction of RUL requires the extraction of multiple performance indicators strongly correlated to system degradation. Accordingly, we propose an RUL prediction framework for a multi-indicator system that considers the effects of random shocks and stochastic dependence. First, considering the influence of random shocks and measurement errors, a degradation state-space model is established using the stochastic correlation of multiple performance indicators. Next, the hidden state and unknown parameters of the state-space model are jointly estimated using the expectation–maximization algorithm in conjunction with the strong tracking filter algorithm based on online monitoring data. Subsequently, the probability density function expression of the RUL is derived for a multi-indicator system with three failure modes. Finally, the accuracy of the RUL prediction model is verified for a system affected by random shocks via numerical experiments. Compared with other prediction methods, the effectiveness and adaptability of the proposed method are verified using the C-MAPSS dataset and a high-temperature furnace case.