Degradation modeling and remaining useful life prediction for a multi-component system with stochastic dependence

Stochastic dependence between components within a system implies that the degradation state of a component influences the lifetime distribution of the other components. The potential failure risks of these components will be underestimated if these effects are ignored. Therefore, the objective of this study was to investigate the effects of the stochastic dependence between components on the degradation process and remaining useful life (RUL) of a system. Firstly, a degradation model integrating the effects of stochastic dependence between components was formulated. Then, the probability density function of the RUL was derived for multi-component systems with different structures. Finally, the dependent degradation state and unknown parameters of the model were estimated simultaneously and recursively using Kalman filtering in conjunction with the expectation maximization algorithm. The superiority of the presented method was confirmed by considering a numerical example and performing case studies of an aircraft turbine engine and a gearbox system.

Automated summary

The study investigates the effects of stochastic dependence between components on the degradation process and remaining useful life (RUL) of a system. A degradation model considering the stochastic dependence between components is formulated. The probability density function of the RUL is derived for multi-component systems with different structures. The dependent degradation state and unknown parameters of the model are estimated simultaneously and recursively using Kalman filtering in conjunction with the expectation maximization algorithm. The superiority of the presented method is confirmed through numerical examples and case studies of an aircraft turbine engine and a gearbox system.