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Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach

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  • Huang, Wencheng
  • Kou, Xingyi
  • Zhang, Yue
  • Mi, Rongwei
  • Yin, Dezhi
  • Xiao, Wei
  • Liu, Zhanru

Abstract

In this paper, a Bayesian Network-K2 Algorithm-Expectation Maximization (BN-K2-EM) approach is proposed to quantify the intensity of coupling influence among the operational failures and find out the specific failure propagation chains in accidents of high-speed electric multiple units, quantitatively. K2 is applied to learn the BN structure because it can effectively learn the structure based on small scale data sets, EM is applied to learn the parameter in BN because it can accurately reflect the probability value among nodes with fast convergence speed. BN-K2-EM belongs to a data-driven method which overcomes the limitation of the logic-based BN approach. BN-K2-EM has 6 steps: establish the failures data matrix; determine the priority of nodes priority based on expanded average causal effect; learn the structure of BN based on K2 algorithm; learn the parameter of BN based on EM algorithm; causal reasoning and predication; reverse reasoning and diagnosis. Finally, a case study is conducted by using the historical high-speed EMU accidents happened in China from 2008 to 2019 as background. The influence strength and sensitivity of each failure mode in the case study are analyzed, and the sensitivity analysis of nodes in each system or sub-system is also conducted.

Suggested Citation

  • Huang, Wencheng & Kou, Xingyi & Zhang, Yue & Mi, Rongwei & Yin, Dezhi & Xiao, Wei & Liu, Zhanru, 2021. "Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:reensy:v:205:y:2021:i:c:s095183202030750x
    DOI: 10.1016/j.ress.2020.107250
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    References listed on IDEAS

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    1. Amin, Md. Tanjin & Khan, Faisal & Imtiaz, Syed, 2018. "Dynamic availability assessment of safety critical systems using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 108-117.
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    3. Oguz, Elif & Kubicek, Martin & Clelland, David, 2018. "Failure modes and criticality analysis of the preliminary design phase of the Mars Desert Research Station considering human factors," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 247-254.
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    Cited by:

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    6. 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).
    7. de Araujo, Matheus Soares & da Silva, Leandro Dias & Sobrinho, Ã lvaro & Cunha, Paulo & Montecchi, Leonardo, 2022. "Reliability analysis of multi-parameter monitoring systems for Intensive Care Units," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Deng, Wanyi, 2023. "Determining the critical risk factors for predicting the severity of ship collision accidents using a data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Xu, Jinjin & Wang, Rongxi & Liang, Zeming & Liu, Pengpeng & Gao, Jianmin & Wang, Zhen, 2023. "Physics-guided, data-refined fault root cause tracing framework for complex electromechanical system," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    10. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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