Resilience-Based Component Importance Measures for Critical Infrastructure Network Systems

• Published in: IEEE transactions on reliability Vol. 65; no. 2; pp. 502 - 512
• Main Authors: Yi-Ping Fang, Pedroni, Nicola, Zio, Enrico
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Agglomeration; Civil Engineering; Component importance measures; Computer industry; critical infrastructure; Electric power; Engineering Sciences; Fault tolerance; Infrastructure; Maintenance engineering; Mathematical models; Networks; Optimization; Resilience; Risques; stochastic ranking; Stochasticity; System performance; system recovery; system resilience; Time delay; Time measurement; system resilience; system recovery; critical infra-structure; Component importance measures; stochastic ranking
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2016.2521761

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.