Title: Multi-state system reliability analysis methods based on Bayesian networks merging dynamic and fuzzy fault information
Authors: Qin He; Ruijun Zhang; Tianyu Liu; Yabing Zha; Jie Liu
Addresses: College of Mechanical and Electrical Engineering, Shandong Jianzhu University, Ji'nan, China ' College of Mechanical and Electrical Engineering, Shandong Jianzhu University, Ji'nan, China ' College of System Engineering, National University of Defense Technology, Changsha, China; Troop 78092 of PLA, Chengdu, China ' College of System Engineering, National University of Defense Technology, Changsha, China ' College of Mechanical and Electrical Engineering, Shandong Jianzhu University, Ji'nan, China
Abstract: Traditional Bayesian Networks (BNs) have limited abilities to analyse system reliability with fuzzy and dynamic information. To deal with such information in system reliability analysis, a new multi-state system reliability analysis method based on BNs was proposed. The proposed method effectively solved the deficiencies of existing reliability analysis methods based on BNs incorporating fuzziness and fault information. In this work, fuzzy set theory and changing failure probability function of components were introduced into BNs, and the dynamic fuzzy subset was introduced. The curve of the fuzzy dynamic fault probability of the leaf node fault state and fuzzy dynamic importance were developed and calculated. Finally, a case study of a truck system was employed to demonstrate the performance of the proposed methods in comparison with traditional fault tree and T-S fuzzy importance analysis methods. The proposed method proved to be feasible in capturing the fuzzy and dynamic information in real-world systems.
Keywords: fuzzy subsets; fuzziness; Bayesian network; travel system of a truck.
International Journal of Reliability and Safety, 2019 Vol.13 No.1/2, pp.44 - 60
Received: 15 Oct 2017
Accepted: 01 Jun 2018
Published online: 03 Dec 2018 *