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Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

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Listed:
  • Ahmed Shokry

    (Center for Applied Mathematics, Ecole Polytechnique, Institut Polytechnique de Paris, Route de Saclay, 91120 Palaiseau, France)

  • Piero Baraldi

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Andrea Castellano

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy)

  • Luigi Serio

    (Engineering Department, CERN, 1211 Geneva, Switzerland)

  • Enrico Zio

    (Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
    Centre de Recherche sur les Risques et les Crises (CRC), MINES ParisTech, PSL Research University, 06904 Sophia Antipolis, France)

Abstract

This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basis of evaluating the performance of a Cost-Sensitive Support Vector Machine (CS-SVM) classifier trained with several subsets of the candidate signals. The capabilities of the methodology proposed are assessed through its application to different benchmarks of highly imbalanced datasets, showing performances that are competitive to those obtained by other methods presented in the literature. The methodology is finally applied to the monitoring signals of the Large Hadron Collider (LHC) of the European Organization for Nuclear Research (CERN), a CTI for experiments of physics; the criticality of the identified components has been confirmed by CERN experts.

Suggested Citation

  • Ahmed Shokry & Piero Baraldi & Andrea Castellano & Luigi Serio & Enrico Zio, 2021. "Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines," Energies, MDPI, vol. 14(18), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:6000-:d:640114
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    References listed on IDEAS

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    1. Patterson, S.A. & Apostolakis, G.E., 2007. "Identification of critical locations across multiple infrastructures for terrorist actions," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1183-1203.
    2. Baraldi, Piero & Castellano, Andrea & Shokry, Ahmed & Gentile, Ugo & Serio, Luigi & Zio, Enrico, 2020. "A Feature Selection-based Approach for the Identification of Critical Components in Complex Technical Infrastructures: Application to the CERN Large Hadron Collider," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. Genge, Béla & Kiss, István & Haller, Piroska, 2015. "A system dynamics approach for assessing the impact of cyber attacks on critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 10(C), pages 3-17.
    4. Antonello, Federico & Baraldi, Piero & Shokry, Ahmed & Zio, Enrico & Gentile, Ugo & Serio, Luigi, 2021. "Association rules extraction for the identification of functional dependencies in complex technical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    5. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    6. Lu, Xuefei & Baraldi, Piero & Zio, Enrico, 2020. "A data-driven framework for identifying important components in complex systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    7. Wu, Baichao & Tang, Aiping & Wu, Jie, 2016. "Modeling cascading failures in interdependent infrastructures under terrorist attacks," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 1-8.
    8. Zio, Enrico, 2016. "Challenges in the vulnerability and risk analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 137-150.
    9. Johansson, Jonas & Hassel, Henrik & Zio, Enrico, 2013. "Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 27-38.
    10. Chopra, Shauhrat S. & Khanna, Vikas, 2015. "Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 865-877.
    11. Zio, E. & Ferrario, E., 2013. "A framework for the system-of-systems analysis of the risk for a safety-critical plant exposed to external events," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 114-125.
    12. Loyola-Fuentes, José & Smith, Robin, 2019. "Data reconciliation and gross error detection in crude oil pre-heat trains undergoing shell-side and tube-side fouling deposition," Energy, Elsevier, vol. 183(C), pages 368-384.
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    Cited by:

    1. Piero Baraldi & Roozbeh Razavi-Far & Enrico Zio, 2023. "Guest Editorial: Special Issue of ESREL2020 PSAM15," Energies, MDPI, vol. 16(4), pages 1-2, February.

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