Details
| Original language | English |
|---|---|
| Title of host publication | Safety and Reliability - Safe Societies in a Changing World |
| Subtitle of host publication | Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018 |
| Editors | Coen van Gulijk, Stein Haugen, Anne Barros, Jan Erik Vinnem, Trond Kongsvik |
| Pages | 2589-2594 |
| Number of pages | 6 |
| Publication status | Published - 2018 |
| Event | 28th International European Safety and Reliability Conference, ESREL 2018 - Trondheim, Norway Duration: 17 Jun 2018 → 21 Jun 2018 |
Publication series
| Name | Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018 |
|---|
Abstract
The effect of natural and man made disasters on critical infrastructures are substantial, as evident from recent history. Break downs of critical systems such as electrical power grids, water supply networks, communication networks or transportation can have dire consequences on the availability of aid in such a crisis. That is why, reliability analyses of these networks are of paramount importance. Two important factors must taken into consideration during reliability analysis. First, the networks are subject to complex interdependencies and must not be treated as individual units. Second, the reliability analysis is typically based on some form of data and or expert knowledge. However, this information is rarely precise or even available. Therefore, it is important to account for different kinds of uncertainties, namely aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty represents the natural randomness in a process, while epistemic uncertainty represents vaguness or lack of knowledge in the model. In this work we present an approach to the numerical reliability analysis of complex networks and systems extending a previously developed method based on Monte Carlo simulation and survival signature. The extended method treats both kinds of uncertainties, thus, yielding better results. We show how Monte Carlo simulation controls aleatory uncertainty and apply sets of distributions (probability boxes) to treat epistemic uncertainties in component failures. In this framework, dependencies are modelled using copulas. Copulas possess the unique property of decoupling the odelling of the univariate margins from the modelling of the dependence structure for continuous multivariate distributions. Analoguous to the p-boxes we use sets of copulas to include imprecision in the dependencies. Finally, the method is applied to an example system of coupled networks.
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Safety and Reliability - Safe Societies in a Changing World: Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018. ed. / Coen van Gulijk; Stein Haugen; Anne Barros; Jan Erik Vinnem; Trond Kongsvik. 2018. p. 2589-2594 (Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Imprecise reliability analysis of complex interconnected networks
AU - Behrensdorf, Jasper
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Publisher Copyright: © 2018 Taylor & Francis Group, London. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - The effect of natural and man made disasters on critical infrastructures are substantial, as evident from recent history. Break downs of critical systems such as electrical power grids, water supply networks, communication networks or transportation can have dire consequences on the availability of aid in such a crisis. That is why, reliability analyses of these networks are of paramount importance. Two important factors must taken into consideration during reliability analysis. First, the networks are subject to complex interdependencies and must not be treated as individual units. Second, the reliability analysis is typically based on some form of data and or expert knowledge. However, this information is rarely precise or even available. Therefore, it is important to account for different kinds of uncertainties, namely aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty represents the natural randomness in a process, while epistemic uncertainty represents vaguness or lack of knowledge in the model. In this work we present an approach to the numerical reliability analysis of complex networks and systems extending a previously developed method based on Monte Carlo simulation and survival signature. The extended method treats both kinds of uncertainties, thus, yielding better results. We show how Monte Carlo simulation controls aleatory uncertainty and apply sets of distributions (probability boxes) to treat epistemic uncertainties in component failures. In this framework, dependencies are modelled using copulas. Copulas possess the unique property of decoupling the odelling of the univariate margins from the modelling of the dependence structure for continuous multivariate distributions. Analoguous to the p-boxes we use sets of copulas to include imprecision in the dependencies. Finally, the method is applied to an example system of coupled networks.
AB - The effect of natural and man made disasters on critical infrastructures are substantial, as evident from recent history. Break downs of critical systems such as electrical power grids, water supply networks, communication networks or transportation can have dire consequences on the availability of aid in such a crisis. That is why, reliability analyses of these networks are of paramount importance. Two important factors must taken into consideration during reliability analysis. First, the networks are subject to complex interdependencies and must not be treated as individual units. Second, the reliability analysis is typically based on some form of data and or expert knowledge. However, this information is rarely precise or even available. Therefore, it is important to account for different kinds of uncertainties, namely aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty represents the natural randomness in a process, while epistemic uncertainty represents vaguness or lack of knowledge in the model. In this work we present an approach to the numerical reliability analysis of complex networks and systems extending a previously developed method based on Monte Carlo simulation and survival signature. The extended method treats both kinds of uncertainties, thus, yielding better results. We show how Monte Carlo simulation controls aleatory uncertainty and apply sets of distributions (probability boxes) to treat epistemic uncertainties in component failures. In this framework, dependencies are modelled using copulas. Copulas possess the unique property of decoupling the odelling of the univariate margins from the modelling of the dependence structure for continuous multivariate distributions. Analoguous to the p-boxes we use sets of copulas to include imprecision in the dependencies. Finally, the method is applied to an example system of coupled networks.
UR - http://www.scopus.com/inward/record.url?scp=85058100025&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85058100025
SN - 9780815386827
T3 - Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference, ESREL 2018
SP - 2589
EP - 2594
BT - Safety and Reliability - Safe Societies in a Changing World
A2 - van Gulijk, Coen
A2 - Haugen, Stein
A2 - Barros, Anne
A2 - Vinnem, Jan Erik
A2 - Kongsvik, Trond
T2 - 28th International European Safety and Reliability Conference, ESREL 2018
Y2 - 17 June 2018 through 21 June 2018
ER -