Imprecise reliability analysis of complex interconnected networks

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dc.identifier.uri Behrensdorf, J. Broggi, M. Beer, M. 2020-01-29T11:58:01Z 2020-01-29T11:58:01Z 2018
dc.identifier.citation Behrensdorf, J.; Broggi, M.; Beer, M.: Imprecise reliability analysis of complex interconnected networks. In: Haugen, S. et al. (Eds.): Safety and Reliability – Safe Societies in a Changing World, 2018, S. 2589-2594. DOI:
dc.description.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. eng
dc.language.iso eng
dc.publisher London : Taylor & Francis Group
dc.relation.ispartofseries Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference, ESREL (2018)
dc.rights CC BY-NC-ND 4.0 Unported
dc.subject Complex networks eng
dc.subject Electric power transmission networks eng
dc.subject Intelligent systems eng
dc.subject Monte Carlo methods eng
dc.subject Numerical methods eng
dc.subject Probability distributions eng
dc.subject Uncertainty analysis eng
dc.subject Water supply eng
dc.subject Aleatory uncertainty eng
dc.subject Dependence structures eng
dc.subject Epistemic uncertainties eng
dc.subject Imprecise reliabilities eng
dc.subject Interconnected network eng
dc.subject Multivariate distributions eng
dc.subject Natural and man-made disasters eng
dc.subject Water supply networks eng
dc.subject Reliability analysis eng
dc.subject.ddc 300 | Sozialwissenschaften, Soziologie, Anthropologie ger
dc.subject.ddc 004 | Informatik ger
dc.title Imprecise reliability analysis of complex interconnected networks
dc.type bookPart
dc.type conferenceObject
dc.type Text
dc.relation.isbn 978-0-8153-8682-7
dc.relation.isbn 978-1-351-17466-4
dc.bibliographicCitation.firstPage 2589
dc.bibliographicCitation.lastPage 2594
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

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