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Impactability and susceptibility assessment based on D-S evidence theory for analyzing the risk of fault propagation among catenary components

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  • Diyang, Liu
  • Shibin, Gao
  • Xiaoguang, Wei
  • Jiaming, Luo
  • Jian, Shi

Abstract

As an important part of the traction power supply system, the research on fault prevention of the catenary system has become a crucial issue for efficient operation and maintenance. In this paper, we propose a data-driven approach to investigate the underlying correlations among catenary components from the historical fault data, so that the fault propagation mechanisms among components can be revealed. Initially, based on the different roles played by components in the fault propagation process, we define fault impactability and susceptibility of components under different mechanical coupling relationships to capture the fault propagation mechanisms. Then, we propose a risk trust function model based on the D-S evidence theory to assess the fault impactability and susceptibility. Meanwhile, a belief and disbelief-based risk coefficient is proposed in the risk trust function model to construct the evidence source. Finally, the case study, based on the fault database of the Chengdu Railway Bureau, demonstrates that the proposed method can effectively assess the fault impactability and susceptibility of components to reveal the fault propagation mechanisms, which provides valuable references for formulating fault prevention strategies.

Suggested Citation

  • Diyang, Liu & Shibin, Gao & Xiaoguang, Wei & Jiaming, Luo & Jian, Shi, 2024. "Impactability and susceptibility assessment based on D-S evidence theory for analyzing the risk of fault propagation among catenary components," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004617
    DOI: 10.1016/j.ress.2024.110389
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    References listed on IDEAS

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    1. Wang, Jian & Gao, Shibin & Yu, Long & Ma, Chaoqun & Zhang, Dongkai & Kou, Lei, 2023. "A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Catelani, Marcantonio & Ciani, Lorenzo & Guidi, Giulia & Patrizi, Gabriele, 2021. "An enhanced SHERPA (E-SHERPA) method for human reliability analysis in railway engineering," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    5. Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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