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

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.

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Publication Details

Journal
International Journal on Semantic Web and Information Systems
Published
2024-10-26
DOI
https://doi.org/10.4018/ijswis.357697
Citations
1
Primary Topic
Risk and Safety Analysis
Type
article
Field-Weighted Citation Impact
0.46
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article

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

Wen Jing, Haibo Tan, Pengcheng Li, Jiayuan He et al.
1 citations
International Journal on Semantic Web and Information Systems
Risk and Safety Analysis
0.46
article

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

Wen Jing, Haibo Tan, Pengcheng Li, Jiayuan He, Ye Wang
article en
1 citations

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.

International Journal on Semantic Web and Information SystemsVol. 20(1)
University of South China (CN), Hunan Institute of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 27%
Risk and Safety Analysis
0.46
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Semantic Web-Based Identification of Key Causative Factors and Chains for Team Error in NPPs' ACR — Wen Jing, Haibo Tan, et al. · International Journal on Semantic Web and Information Systems (2024) | TGRS Research Map | TGRS