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Bayesian network-based human error reliability assessment of derailments

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  • Dindar, Serdar
  • Kaewunruen, Sakdirat
  • An, Min

Abstract

The knowledge acquired in relation to failures associated with components has made significant contributions to the development of components with increased reliability, as well as a reduction in the number of rail incidents caused by certain system defects. These new systems have led to innovative developments in both the operations and technology of rail networks. Hence, rail employees must now function in conditions that have high complexity that are hard to comprehend. The risk of failure caused by human error (such as by dispatchers, train crews and track engineers) has developed into a significant safety problem. This study is the world first to provide novel insights into better understanding human errors, which result in derailments at rail turnouts. A most- to-least-critical importance ranking of these errors is established throughout a novel risk management technique. Moreover, the new findings and recommendations of this research study have a strong potential for industry to improve the reliability of rail operation, and avoid safety concerns regarding train derailments at rail turnouts.

Suggested Citation

  • Dindar, Serdar & Kaewunruen, Sakdirat & An, Min, 2020. "Bayesian network-based human error reliability assessment of derailments," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:reensy:v:197:y:2020:i:c:s0951832019308233
    DOI: 10.1016/j.ress.2020.106825
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    References listed on IDEAS

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    1. Read, G.J.M. & Naweed, A. & Salmon, P.M., 2019. "Complexity on the rails: A systems-based approach to understanding safety management in rail transport," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 352-365.
    2. Serdar Dindar & Sakdirat Kaewunruen & Min An, 2018. "Identification of appropriate risk analysis techniques for railway turnout systems," Journal of Risk Research, Taylor & Francis Journals, vol. 21(8), pages 974-995, August.
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    Cited by:

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    2. Jang, Inseok & Kim, Yochan & Park, Jinkyun, 2021. "Investigating the Effect of Task Complexity on the Occurrence of Human Errors observed in a Nuclear Power Plant Full-Scope Simulator," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. 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).
    4. Song, Bing & Zhang, Zhipeng & Qin, Yong & Liu, Xiang & Hu, Hao, 2022. "Quantitative analysis of freight train derailment severity with structured and unstructured data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Zhou, Jian-Lan & Tu, Ren-Fang & Xiao, Hai, 2022. "Large-scale group decision-making to facilitate inter-rater reliability of human-factors analysis for the railway system," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Dindar, Serdar & Kaewunruen, Sakdirat & An, Min, 2022. "A hierarchical Bayesian-based model for hazard analysis of climate effect on failures of railway turnout components," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Zhou, Jian-Lan & Yu, Ze-Tai & Xiao, Ren-Bin, 2022. "A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    8. Ji, Chenyi & Su, Xing & Qin, Zhongfu & Nawaz, Ahsan, 2022. "Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    9. Rungskunroch, Panrawee & Jack, Anson & Kaewunruen, Sakdirat, 2021. "Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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