Numerically efficient computation of the survival signature for the reliability analysis of large networks

Behrensdorf, Jasper, Regenhardt, Tobias-Emanuel, Broggi, Matteo and Beer, Michael ORCID: 0000-0002-0611-0345 (2021) Numerically efficient computation of the survival signature for the reliability analysis of large networks. RELIABILITY ENGINEERING & SYSTEM SAFETY, 216. p. 107935.

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Official URL: http://dx.doi.org/10.1016/j.ress.2021.107935

Abstract

Societal growth thrives on the performance of critical infrastructure systems such as water supply systems, transportation networks or electrical distribution systems. This makes the reliability analysis of these systems a core focus for researchers today. The survival signature is a novel tool for analysing complex networks efficiently and outperforms traditional techniques in several key factors. Its most unique feature being a full separation of the system structure from probabilistic information. This in turn allows for the consideration of diverse component failure descriptions such as dependencies, common causes of failure and imprecise probabilities. However, the numerical effort to compute the survival signature increases with network size and prevents analysis of complex systems. This work presents a new method to approximate the survival signature, where system configurations of low interest are first excluded using percolation theory, while the remaining parts of the signature are approximated by Monte Carlo simulation. The approach is able to accurately approximate the survival signature with very small error at a massive reduction in computational demands. The accuracy and performance are highlighted using several simple test systems as well as two real world problems.

Item Type:	Article
Uncontrolled Keywords:	Survival signature, Monte Carlo simulation, Percolation, Reliability analysis
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	19 Aug 2021 07:34
Last Modified:	18 Jan 2023 21:33
DOI:	10.1016/j.ress.2021.107935
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3133920