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Fault information mining with causal network for railway transportation system

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  • Liu, Jie
  • Xu, Yubo
  • Wang, Lisong

Abstract

Various sensors implemented in the railway transportation system brings opportunities in improving its safety and challenges in fault information mining. Extracting effective and synthetic fault-specific information from the over-rich data is one of the key challenges. The classical feature dimension reduction methods are mostly based on the statistical correlation among variables. Considering the cause-effect relationship may reflect the true influence of one variable on the other, this paper proposes three unsupervised feature extraction methods based on causal network. Precisely, after discovering the causal network among the monitoring variables in a rail transportation system, principle components related to the specific fault are extracted from the causal strength matrix or the full causal strength matrix constructed from the causal network. In comparison with the state-of-art correlation-based feature reduction methods, the effectiveness of the proposed methods is verified on two public datasets and a real dataset considering high-speed train braking system. In addition, the intrinsic working mechanism of the proposed methods is analyzed with respect to the constructed causal network, which improves the interpretability of the fault detection and diagnosis.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:220:y:2022:i:c:s0951832021007535
    DOI: 10.1016/j.ress.2021.108281
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    2. Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Javier, Prince Joseph Erneszer A. & Liponhay, Marissa P. & Dajac, Carlo Vincienzo G. & Monterola, Christopher P., 2022. "Causal network inference in a dam system and its implications on feature selection for machine learning forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    4. Ma, Xiaoxue & Deng, Wanyi & Qiao, Weiliang & Lan, He, 2022. "A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. Liu, Jie & Zheng, Shuwen & Wang, Chong, 2023. "Causal Graph Attention Network with Disentangled Representations for Complex Systems Fault Detection," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Zheng, Niannian & Luan, Xiaoli & Shardt, Yuri A.W. & Liu, Fei, 2024. "Dynamic-controlled principal component analysis for fault detection and automatic recovery," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Shen, Yang & Yang, Zhen & Guo, Li & Zhao, Xiaozhe & Duan, Yao, 2024. "Scenario mapping for critical infrastructure failure under typhoon rainfall: A dependency and causality approach," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

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