Reliability analysis of self-healing systems equipped with multi-component protective devices operating in a shock environment

Xiaoyue Wang, Xi Chen, Xian Zhao and Ru Ning

Reliability Engineering and System Safety, 2024, vol. 244, issue C

Abstract: Many industrial systems, possess the self-healing abilities owing to the use of self-healing materials, such as the pipelines with the self-healing coatings. Furthermore, protective devices are installed for the self-healing systems due to the severity of the shock environment. However, no prior studies have evaluated the reliability of self-healing systems equipped with protective devices. To bridge this research gap, this study first investigates the reliability of self-healing systems supported by multi-component protective devices in a shock environment. When a run of invalid shocks with a certain size occurs, the self-healing mechanism of the system is activated to repair the damage resulting from previous valid shocks. Multi-component protective devices have been utilized for the systems in the hostile shock environments to reduce the risk of failure. When the self-healing mechanism and protection from the devices exert a joint influence on the system, a scenario where the protective devices switch between operating and standby states may occur. The reliability indexes of the self-healing system with multi-component protective devices are analyzed in the case of the shock length and continuous time by a combined method. The applicability of the proposed model and efficacy of the method are demonstrated by numerical examples.

Keywords: Self-healing system; Multi-component protective device; Reliability analysis; Finite Markov chain imbedding approach; Phase-type distribution (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:244:y:2024:i:c:s0951832023007585

DOI: 10.1016/j.ress.2023.109844

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