TY - GEN

T1 - Efficient Approximation of the Survival Signature for Large Networks

AU - Behrensdorf, Jasper

AU - Brandt, Sebastian

AU - Broggi, Matteo

AU - Beer, Michael

PY - 2018

Y1 - 2018

N2 - The reliability analysis of complex networks, e.g. water supply networks, transportation networks or electrical distribution networks, is of key importance to the resilience of communities. The concept of survival signature provides a novel basis for analyzing complex networks efficiently. The survival signature outperforms traditional analyses techniques, in particular, when estimating the reliability of networks. Its most unique feature is the separation of the network structure from its probabilistic properties, opening pathways for the consideration of, for instance, general dependencies, common cause failures, or vaguely specified probabilities. However, the numerical effort to calculate the survival signature is still prohibitive for large systems. While the issue of numerical efficiency can be addressed well with analytical approaches such as the use of binary decision diagrams, these approaches are limited by the number of components and types. In this paper we propose an approximation of the survival signature using a combination of graph theory and Monte Carlo simulation. By application of graph theory, we are able to predetermine certain fractions of the survival signature without explicitly evaluating it. The remaining fraction is then analyzed with Monte Carlo simulation in a targeted manner, circumventing high-effort-low-contribution calculations. The developed approach excels, in particular, in cases with a large number of different component types. Using an example we highlight the significant reduction in computational effort required to accurately determine the survival signature.

AB - The reliability analysis of complex networks, e.g. water supply networks, transportation networks or electrical distribution networks, is of key importance to the resilience of communities. The concept of survival signature provides a novel basis for analyzing complex networks efficiently. The survival signature outperforms traditional analyses techniques, in particular, when estimating the reliability of networks. Its most unique feature is the separation of the network structure from its probabilistic properties, opening pathways for the consideration of, for instance, general dependencies, common cause failures, or vaguely specified probabilities. However, the numerical effort to calculate the survival signature is still prohibitive for large systems. While the issue of numerical efficiency can be addressed well with analytical approaches such as the use of binary decision diagrams, these approaches are limited by the number of components and types. In this paper we propose an approximation of the survival signature using a combination of graph theory and Monte Carlo simulation. By application of graph theory, we are able to predetermine certain fractions of the survival signature without explicitly evaluating it. The remaining fraction is then analyzed with Monte Carlo simulation in a targeted manner, circumventing high-effort-low-contribution calculations. The developed approach excels, in particular, in cases with a large number of different component types. Using an example we highlight the significant reduction in computational effort required to accurately determine the survival signature.

KW - Networks

KW - reliability

KW - Monte Carlo simulation

KW - survival signature

U2 - 10.3850/978-981-11-2726-7_crr14

DO - 10.3850/978-981-11-2726-7_crr14

M3 - Conference contribution

SN - 978-981-11-2726-7

SP - 661

EP - 666

BT - Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management

CY - Singapore

ER -