Numerically efficient computation of the survival signature for the reliability analysis of large networks
- authored by
- Jasper Behrensdorf, Tobias-Emanuel Regenhardt, Matteo Broggi, Michael Beer
- 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.
- Organisation(s)
-
Institute for Risk and Reliability
- External Organisation(s)
-
University of Liverpool
Tongji University
International Joint Research Center for Engineering Reliability and Stochastic Mechanics
- Type
- Article
- Journal
- Reliability engineering & system safety
- Volume
- 216
- ISSN
- 0951-8320
- Publication date
- 12.2021
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality, Industrial and Manufacturing Engineering
- Electronic version(s)
-
https://doi.org/10.1016/j.ress.2021.107935 (Access:
Closed)
