详细信息
Decision-oriented reliability assessment of complex mechatronic systems based on stochastic flow networks ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:Decision-oriented reliability assessment of complex mechatronic systems based on stochastic flow networks
作者:Lin, Shuai[1];Liu, Cuifang[1];Jia, Limin[2];Zhang, Hengrun[3];Zhang, Pengzhu[4];Yu, Benhai[1]
机构:[1]Shanghai Inst Technol, Shanghai, Peoples R China;[2]Beijing Jiaotong Univ, Beijing, Peoples R China;[3]East China Univ Sci & Technol, Shanghai, Peoples R China;[4]Shanghai Jiao Tong Univ, Shanghai, Peoples R China
年份:2026
卷号:28
期号:4
起止页码:1
外文期刊名:EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
收录:;EI(收录号:20262520930685);WOS:【SCI-EXPANDED(收录号:WOS:001795871600021)】;
基金:This work is supported by the Shanghai Municipal Philosophy and Social Sciences Planning Youth Project (Grant: 2025EGL003) .
语种:英文
外文关键词:system reliability; complex mechatronic systems; stochastic flow networks; fuzzy theory; management decision-making
摘要:Conventional reliability assessments for complex mechatronic systems (CMS) often fail to fully account for the specific demands of operational management and maintenance decision-making. To bridge this gap, a hierarchical system reliability modeling approach that combines stochastic flow networks with fuzzy logic theory is introduced. Specifically, a multi-layer stochastic flow network (MSFN) is constructed to simultaneously capture the holistic topology and its dynamic functional execution mechanisms. In this model, minimal paths (MPs) are employed to calculate layer reliability under flow constraints. Concurrently, the 2-additive Choquet integral functions as an aggregation operator to quantify dependencies between different layers. Furthermore, to reduce cognitive uncertainty in expert elicitation, interval hesitant fuzzy sets are combined with the Mahalanobis-Taguchi System. Finally, a functional operator is constructed for system reliability assessment across different decision-making scenarios. Application to an urban rail train system confirms the effectiveness of the proposed method in providing targeted decision support across diverse management departments.
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