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Development of a quantitative Bayesian network mapping objective factors to subjective performance shaping factor evaluations: An example using student operators in a digital nuclear power plant simulator

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  • Shirley, Rachel Benish
  • Smidts, Carol
  • Zhao, Yunfei

Abstract

Traditional human reliability analysis methods consist of two main steps: assigning values for performance shaping factors (PSFs), and assessing human error probability (HEP) based on PSF values. Both steps rely on expert judgment. Considerable advances have been made in reducing reliance on expert judgment for HEP assessment by incorporating human performance data from various sources (e.g., simulator experiments); however, little has been done to reduce reliance on expert judgment for PSF assignment. This paper introduces a data-driven approach for assessing PSFs in Nuclear Power Plants (NPPs) based on contextual information. The research illustrates how to develop a Bayesian PSF network using data collected from student operators in a NPP simulator. The approach starts with a baseline PSF model that calculates PSF values from context information during an accident scenario. Then, a Bayesian model is developed to link the baseline model to the Subjective PSFs. Two additional factors are included: simulator bias and context information. Results and analysis include variation between the results of the proposed model and the training dataset, and the significance of each element in the model. The proposed approach reduces the reliance of PSF assignment on expert judgment and is particularly suitable for dynamic human reliability analysis.

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  • Shirley, Rachel Benish & Smidts, Carol & Zhao, Yunfei, 2020. "Development of a quantitative Bayesian network mapping objective factors to subjective performance shaping factor evaluations: An example using student operators in a digital nuclear power plant simul," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:reensy:v:194:y:2020:i:c:s0951832017310311
    DOI: 10.1016/j.ress.2019.03.023
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    1. Zhao, Yunfei & Smidts, Carol, 2021. "CMS-BN: A cognitive modeling and simulation environment for human performance assessment, part 2 — Application," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Che, Haiyang, 2021. "A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    3. Zhou, Jian-Lan & Lei, Yi, 2020. "A slim integrated with empirical study and network analysis for human error assessment in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Zhao, Yunfei, 2022. "A Bayesian approach to comparing human reliability analysis methods using human performance data," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Zhao, Yunfei & Smidts, Carol, 2021. "CMS-BN: A cognitive modeling and simulation environment for human performance assessment, part 1 — methodology," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. 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).

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