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

• Published in: Computers & industrial engineering 2023-01, Vol.175, p.108889, Article 108889
• Main Authors: Niu, Huifang, Zeng, Jianchao, Shi, Hui, Zhang, Xiaohong, Liang, Jianyu
• Format: Article
• Language: English
• Subjects: Degradation modeling; Multi-component systems; Prediction; Remaining useful life; Stochastic dependence
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2022.108889

•A degradation model integrating the impact of stochastic dependence of components is proposed.•The hidden states and unknown parameters are jointly estimated using Kalman filtering and EM algorithm.•Remaining useful life (RUL) is predicted with considering stochastic dependence.•The expression of the RUL PDF is derived for a multi-component system with different structures.•An application of the proposed method to a case study of the gearbox system is introduced. 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.