A reliability model for systems subject to mutually dependent degradation processes and random shocks under dynamic environments

• Published in: Reliability engineering & system safety Vol. 234; p. 109165
• Main Authors: Liang, Qingzhu, Yang, Yinghao, Peng, Changhong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2023
• Subjects: Degradation modeling; Dependency; Dynamic environments; Random shocks; Reliability; Dependency; Reliability; Random shocks; Dynamic environments; Degradation modeling
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2023.109165

•A reliability for systems subject to mutually dependent degradation processes are proposed.•The effect of random shocks and dynamic environments are considered.•Closed-form of reliability measures of the system with/without consideration of maintenance are derived.•A Monte Carlo (MC) simulation algorithm is given to verify the correctness of the analytical model. The performance of many products may be simultaneously governed by multiple internal degradation processes, random shocks, and dynamic environments. This article presents a reliability model for systems subject to two degradation processes and random shocks under dynamic environments. Both multi-state and continuous degradation processes are considered and described by a Semi-Markov process and a monotone stochastic process, respectively. The model concurrently addressed the mutual degradation dependency and the effect of random shocks on the degradation processes. In addition, the influences of the environment on the degradation processes and random shocks were considered. Closed-form of reliability measures, such as reliability function and expected remaining useful life of the system with/without consideration of maintenance, were derived. The analytical model was applied to a study case from the literature to demonstrate its feasibility. We proposed a Monte Carlo (MC) simulation algorithm as an alternative solution for estimating system reliability measures. The results of the MC method and the literature verified the correctness of the theoretical result of the analytical model.