Marginal and joint failure importance for K-terminal network edges under counting process

Chengye Ma, Yongjun Du, Yuchun Zhang and Zhiqiang Cai

Reliability Engineering and System Safety, 2022, vol. 223, issue C

Abstract: Importance measures have been applied extensively in various fields such as reliability optimization, failure diagnosis, and risk analysis. Traditional importance measures are insufficient for networks because they only quantify the contribution of each edge's reliability to network reliability. The counting process is a kind of stochastic process that can count the number of edge failures appeared in a time of period, which requires less information than knowing all edge reliabilities. In the context of edge failures subject to a counting process, this paper investigates importance measures for a given binary K-terminal network including n binary edges. This paper develops some formulas to compute the joint failure importance (JFI) and the marginal failure importance (MFI). The MFI quantifies the changes of network failure probability caused by the change of edge state, while the JFI evaluates the interaction effect between two edges regarding network failure probability. Their values, as functions of time t, depend on the probability distribution of the total number of edge failures at time t and the network structure. Additionally, we present a Monte-Carlo algorithm to approximate the values of the MFI and JFI. Finally, a numerical example concerning the road network is provided to demonstrate the computation methods of MFI and JFI, whose edge failures are subject to a saturated nonhomogeneous Poisson process. The numerical results provide further insights for the road network regarding the importance of edges.

Keywords: K-terminal network; Counting process; Nonhomogeneous Poisson process; Marginal failure importance; Joint failure importance (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022001028

DOI: 10.1016/j.ress.2022.108436

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