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Association rules extraction for the identification of functional dependencies in complex technical infrastructures

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  • Antonello, Federico
  • Baraldi, Piero
  • Shokry, Ahmed
  • Zio, Enrico
  • Gentile, Ugo
  • Serio, Luigi

Abstract

This work proposes a method for identifying functional dependencies among components of complex technical infrastructures using databases of alarm messages. The developed method is based on the representation of the alarm database by a binary matrix, the use of the Apriori algorithm for mining association rules and a new algorithm for identifying groups of functionally dependent components. The effectiveness of the proposed method is shown by means of its application to an artificial case study and a real large-scale database of alarms generated by different supervision systems of the complex technical infrastructure of CERN (European Organization for Nuclear Research).

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832020308012
    DOI: 10.1016/j.ress.2020.107305
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    References listed on IDEAS

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    1. Eusgeld, Irene & Nan, Cen & Dietz, Sven, 2011. "“System-of-systems†approach for interdependent critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 679-686.
    2. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    3. O’Connor, Andrew & Mosleh, Ali, 2016. "A general cause based methodology for analysis of common cause and dependent failures in system risk and reliability assessments," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 341-350.
    4. Bevilacqua, Maurizio & Ciarapica, Filippo Emanuele, 2018. "Human factor risk management in the process industry: A case study," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 149-159.
    5. Johansson, Jonas & Hassel, Henrik, 2010. "An approach for modelling interdependent infrastructures in the context of vulnerability analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1335-1344.
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    Cited by:

    1. Hao, Yucheng & Jia, Limin & Zio, Enrico & Wang, Yanhui & He, Zhichao, 2023. "A multi-objective optimization model for identifying groups of critical elements in a high-speed train," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. 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.
    4. Hu, Xing & Yu, Shiwei & Fang, Xu & Ovaere, Marten, 2023. "Which combinations of renewable energy policies work better? Insights from policy text synergies in China," Energy Economics, Elsevier, vol. 127(PA).

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