A stochastic approach for the reliability evaluation of multi-state systems with dependent components

Xiaogang Song, Zhengjun Zhai, Yidong Liu and Jie Han

Reliability Engineering and System Safety, 2018, vol. 170, issue C, 257-266

Abstract: A multi-state system (MSS) employs more than two discrete states to indicate different performance rates. Methods using a universal generating function (UGF) and Monte Carlo (MC) simulation are primary approaches for the reliability analysis of an MSS. However, these approaches incur a large computational overhead because the number of system states increases significantly with the number of components in an MSS. In this paper, stochastic multi-valued (SMV) models are proposed for evaluating the reliability of an MSS with dependent multi-state components (MSCs). The performance rates and their corresponding probabilities of the MSCs are simultaneously encoded in multi-valued non-Bernoulli sequences using permutations of fixed numbers of 1 s and 0 s. The sequences are then processed by logic gates. The effectiveness of the proposed approach is demonstrated via a comparative evaluation of a multi-state system consisting of dependent components with steady and time-varying state probabilities.

Keywords: Multi-state system; Dependent multi-state component; Stochastic multi-valued model (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:170:y:2018:i:c:p:257-266

DOI: 10.1016/j.ress.2017.10.015

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