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A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

Author

Listed:
  • Federico Antonello

    (Politecnico di Milano)

  • Piero Baraldi

    (Politecnico di Milano)

  • Enrico Zio

    (Politecnico di Milano
    PSL Research University, CRC)

  • Luigi Serio

    (CERN)

Abstract

In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

Suggested Citation

  • Federico Antonello & Piero Baraldi & Enrico Zio & Luigi Serio, 2022. "A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data," Environment Systems and Decisions, Springer, vol. 42(2), pages 177-188, June.
    • Handle: RePEc:spr:envsyd:v:42:y:2022:i:2:d:10.1007_s10669-021-09841-z
    DOI: 10.1007/s10669-021-09841-z
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    References listed on IDEAS

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    1. Rebello, Sinda & Yu, Hongyang & Ma, Lin, 2018. "An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 124-135.
    2. R. Cantelmi & G. Di Gravio & R. Patriarca, 2021. "Reviewing qualitative research approaches in the context of critical infrastructure resilience," Environment Systems and Decisions, Springer, vol. 41(3), pages 341-376, September.
    3. 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.
    4. Thacker, Scott & Pant, Raghav & Hall, Jim W., 2017. "System-of-systems formulation and disruption analysis for multi-scale critical national infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 30-41.
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    Cited by:

    1. Federico Antonello & Piero Baraldi & Enrico Zio & Luigi Serio, 2022. "A Novel Metric to Evaluate the Association Rules for Identification of Functional Dependencies in Complex Technical Infrastructures," Environment Systems and Decisions, Springer, vol. 42(3), pages 436-449, September.
    2. Mustafa, Faizan E & Ahmed, Ijaz & Basit, Abdul & Alvi, Um-E-Habiba & Malik, Saddam Hussain & Mahmood, Atif & Ali, Paghunda Roheela, 2023. "A review on effective alarm management systems for industrial process control: Barriers and opportunities," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).

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