IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v205y2021ics095183202030750x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202030750X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2020.107250?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sajid, Zaman & Khan, Faisal & Zhang, Yan, 2017. "Integration of interpretive structural modelling with Bayesian network for biodiesel performance analysis," Renewable Energy, Elsevier, vol. 107(C), pages 194-203.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. Huang, Wencheng & Zhang, Yue & Yin, Dezhi & Zuo, Borui & Liu, Zhanru, 2021. "Urban bus accident analysis: based on a Tropos Goal Risk-Accident Framework considering Learning From Incidents process," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. 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).
    8. Chen, Tianyi & Wong, Yiik Diew & Shi, Xiupeng & Wang, Xueqin, 2022. "Optimized structure learning of Bayesian Network for investigating causation of vehicles’ on-road crashes," Reliability Engineering and System Safety, Elsevier, vol. 224(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sajid, Zaman, 2021. "A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    3. Zhou, Jianxiong & Wei, Shanbi & Chai, Yi, 2021. "Using improved dynamic Bayesian networks in reliability evaluation for flexible test system of aerospace pyromechanical device products," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. Naim Ahmad & Ayman Qahmash, 2021. "SmartISM: Implementation and Assessment of Interpretive Structural Modeling," Sustainability, MDPI, vol. 13(16), pages 1-27, August.
    5. Zaman Sajid & Nicholas Lynch, 2018. "Financial Modelling Strategies for Social Life Cycle Assessment: A Project Appraisal of Biodiesel Production and Sustainability in Newfoundland and Labrador, Canada," Sustainability, MDPI, vol. 10(9), pages 1-19, September.
    6. Xu, Jintao & Gui, Maolei & Ding, Rui & Dai, Tao & Zheng, Mengyan & Men, Xinhong & Meng, Fanpeng & Yu, Tao & Sui, Yang, 2023. "A new approach for dynamic reliability analysis of reactor protection system for HPR1000," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C. & Ariffin, A.K. & Singh, S.S., 2021. "Evidence based risk analysis of fire and explosion accident scenarios in FPSOs," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Tsoumpris, Charalampos & Theotokatos, Gerasimos, 2023. "A decision-making approach for the health-aware energy management of ship hybrid power plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    9. Yi Yang & John Dalsgaard Sørensen, 2020. "Probabilistic Availability Analysis for Marine Energy Transfer Subsystem Using Bayesian Network," Energies, MDPI, vol. 13(19), pages 1-27, October.
    10. Kai Yu & Lujie Zhou & Pingping Liu & Jing Chen & Dejun Miao & Jiansheng Wang, 2022. "Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    11. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui, 2023. "Dynamic Bayesian network model to study under-deposit corrosion," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Fuli Zhou & Yandong He & Felix T. S. Chan & Panpan Ma & Francesco Schiavone, 2022. "Joint Distribution Promotion by Interactive Factor Analysis using an Interpretive Structural Modeling Approach," SAGE Open, , vol. 12(1), pages 21582440221, February.
    13. Zaman Sajid & Asma Javaid, 2018. "A Stochastic Approach to Energy Policy and Management: A Case Study of the Pakistan Energy Crisis," Energies, MDPI, vol. 11(9), pages 1-18, September.
    14. Haifeng Bian & Jun Zhang & Ruixue Li & Huanhuan Zhao & Xuexue Wang & Yiping Bai, 2021. "Risk analysis of tripping accidents of power grid caused by typical natural hazards based on FTA-BN model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 1771-1795, April.
    15. Dong-Kyeong Lee & Yebin Lee & Byungwoon Park, 2023. "Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning," Mathematics, MDPI, vol. 11(12), pages 1-21, June.
    16. Taleb-Berrouane, Mohammed & Khan, Faisal & Amyotte, Paul, 2020. "Bayesian Stochastic Petri Nets (BSPN) - A new modelling tool for dynamic safety and reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    17. Liu, Zengkai & Ma, Qiang & Cai, Baoping & Shi, Xuewei & Zheng, Chao & Liu, Yonghong, 2022. "Risk coupling analysis of subsea blowout accidents based on dynamic Bayesian network and NK model," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    18. Zhao, Zhen-Yu & Chen, Yu-Long & Li, Heng, 2019. "What affects the development of renewable energy power generation projects in China: ISM analysis," Renewable Energy, Elsevier, vol. 131(C), pages 506-517.
    19. Zhao, Xian & Li, Rong & Cao, Shuai & Qiu, Qingan, 2023. "Joint modeling of loading and mission abort policies for systems operating in dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    20. Huang, Wencheng & Zhang, Yue & Kou, Xingyi & Yin, Dezhi & Mi, Rongwei & Li, Linqing, 2020. "Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:205:y:2021:i:c:s095183202030750x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.