Resilience-Based Component Importance Measures for Critical Infrastructure Network Systems

Abstract : In this paper, we propose two metrics, i.e., the optimal repair time and the resilience reduction worth, to measure the criticality of the components of a network system from the perspective of their contribution to system resilience. Specifically, the two metrics quantify: 1) the priority with which a failed component should be repaired and re-installed into the network and 2) the potential loss in the optimal system resilience due to a time delay in the recovery of a failed component, respectively. Given the stochastic nature of disruptive events on infrastructure networks , a Monte Carlo-based method is proposed to generate probability distributions of the two metrics for all of the components of the network; then, a stochastic ranking approach based on the Copeland's pairwise aggregation is used to rank components importance. Numerical results are obtained for the IEEE 30-bus test network and a comparison is made with three classical centrality measures.
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IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers, 2016, 65 (2), pp.502-512. 〈10.1109/TR.2016.2521761〉
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Yi-Ping Fang, Nicola Pedroni, Enrico Zio. Resilience-Based Component Importance Measures for Critical Infrastructure Network Systems. IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers, 2016, 65 (2), pp.502-512. 〈10.1109/TR.2016.2521761〉. 〈hal-01275074〉

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