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Semantic Web-Based Identification of Key Causative Factors and Chains for Team Error in NPPs' ACR

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  • Jing Wen

    (University of South China, China & Hunan Institute of Technology, China)

  • Jiayuan He

    (University of South China, China)

  • Ye Wang

    (University of South China, China)

  • Haibo Tan

    (China Nuclear Power Engineering Co., Ltd., China)

  • Pengcheng Li

    (University of South China, China)

Abstract

Team errors are identified as a major contributor to incidents in safety-critical systems like nuclear power plants (NPPs). Safety in NPPs is more dependent on team performance than individual, and reducing team errors requires identifying key causative factors and chains. Current studies primarily use qualitative analysis whereas lacking quantitative methods. This study addressed this by constructing a complex network model of team error based on semantic features and coding of the Semantic Web and determining criteria for selecting key causative factors by three node failure strategies. The top 5 team error key PSFs and top 5 high-risk causative chains were identified in the results. Finally, a set of control strategies was proposed further. This research can guide identifying key causative factors which could be a good starting point to scrutinize the team performance and establish control strategies for team error in NPPs.

Suggested Citation

  • Jing Wen & Jiayuan He & Ye Wang & Haibo Tan & Pengcheng Li, 2024. "Semantic Web-Based Identification of Key Causative Factors and Chains for Team Error in NPPs' ACR," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-27, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-27
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