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A data-driven framework for identifying important components in complex systems

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  • Lu, Xuefei
  • Baraldi, Piero
  • Zio, Enrico

Abstract

Complex technical infrastructures are systems of systems characterized by hierarchical structures, made by thousands of mutually interconnected components performing different functions. Given their complexity, it is difficult to derive their functional logic using traditional risk and reliability analysis methods based on engineering knowledge. In this work, we propose to address the problem in an innovative way that makes use of the large amount of data available from monitoring those systems. Specifically, we develop a data-driven framework to identify the critical components of a complex technical infrastructure. The criticality of a component with respect to the safe/failed state of the infrastructure is assessed considering a feature selection technique which employs Random Forest (RF) classification and a feature importance score. The proposed data-driven framework is applied to a nuclear power plant system and a synthetic case study, which mimics the complexity of a technical infrastructure.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306980
    DOI: 10.1016/j.ress.2020.107197
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    References listed on IDEAS

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    Cited by:

    1. Liu, Jie & Xu, Yubo & Wang, Lisong, 2022. "Fault information mining with causal network for railway transportation system," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. 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).
    3. Phuc Do & Christophe Bérenguer, 2022. "Residual life-based importance measures for predictive maintenance decision-making," Journal of Risk and Reliability, , vol. 236(1), pages 98-113, February.
    4. Floreale, Giovanni & Baraldi, Piero & Lu, Xuefei & Rossetti, Paolo & Zio, Enrico, 2024. "Sensitivity analysis by differential importance measure for unsupervised fault diagnostics," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Takeda, Satoshi & Kitada, Takanori, 2023. "Importance measure evaluation based on sensitivity coefficient for probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Wang, Yang & Chen, Peng & Wu, Bing & Wan, Chengpeng & Yang, Zaili, 2022. "A trustable architecture over blockchain to facilitate maritime administration for MASS systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. 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.
    8. Vaisman, Radislav & Sun, Yuting, 2021. "Reliability and importance measure analysis of networks with shared risk link groups," Reliability Engineering and System Safety, Elsevier, vol. 211(C).

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