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Enhanced reliability analysis method for multistate systems with epistemic uncertainty based on evidential network
Author(s) -
Zhang Zhe,
Chen Ziwei,
Jiang Chao
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2735
Subject(s) - reliability (semiconductor) , component (thermodynamics) , set (abstract data type) , evidential reasoning approach , reliability engineering , frame (networking) , computer science , belief structure , uncertainty analysis , function (biology) , power (physics) , mathematics , algorithm , mathematical optimization , data mining , engineering , artificial intelligence , decision support system , simulation , telecommunications , business decision mapping , physics , quantum mechanics , evolutionary biology , biology , thermodynamics , programming language
Evidential network is considered to have superiority in conducting reliability analysis for complex engineering systems with epistemic uncertainty. However, existing methods tend to result in combinational explosion when multistate systems are involved in the reliability analysis, which means the reliability analysis cost increases exponentially with the number of components and that of functioning states. Therefore, an enhanced reliability analysis method is proposed in this paper for reliability analysis and performance evaluation of multistate systems with epistemic uncertainty, through which the combination explosion can be significantly alleviated. Firstly, the functioning states of each component are sequenced according to utility functions. Secondly, the basic belief assignment (BBA) of each component is reassigned in terms of commonality function, through which the BBA defined in the power set space is represented by two extreme BBA distributions defined in the frame of discernment. Thirdly, the reliability intervals of the system states are calculated through evidential network, and the system performance level is computed. Two multistate system numerical examples are investigated to demonstrate the effectiveness and efficiency of the proposed method.