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Incorporating prior knowledge with simulation data to estimate PSF multipliers using Bayesian logistic regression

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  • Kim, Yochan
  • Park, Jinkyun

Abstract

Recently, several kinds of databases have been constructed and analyzed to support human reliability analyses. Based on these, some researchers have attempted to model the quantitative relations between performance shaping factors and human error probability. However, the limitations of the traditional regression technique and simulation data employed have come to light. To tackle these issues regarding the traditional statistical analysis, this study proposes an analysis based on the Bayesian logistic regression method that incorporates empirical data with prior knowledge. This method was applied to four different prior knowledge sets and empirical data collected via the Human Reliability data Extraction (HuREX) framework. The mean and credible interval from the obtained posterior distributions were compared with previous research. From the application, we found that the suggested approach is useful in consolidating various data sources to estimate the multipliers of performance shaping factors on error probabilities, producing results robust to the data characteristics, and providing the quantitative uncertainties of the estimation. It is also confirmed that selecting an appropriate prior knowledge and collecting abundant and correct empirical data are important for producing meaningful insights for PSF impacts.

Suggested Citation

  • Kim, Yochan & Park, Jinkyun, 2019. "Incorporating prior knowledge with simulation data to estimate PSF multipliers using Bayesian logistic regression," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 210-217.
    • Handle: RePEc:eee:reensy:v:189:y:2019:i:c:p:210-217
    DOI: 10.1016/j.ress.2019.04.022
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    References listed on IDEAS

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    8. Kim, Yochan & Park, Jinkyun & Jung, Wondea & Choi, Sun Yeong & Kim, Seunghwan, 2018. "Estimating the quantitative relation between PSFs and HEPs from full-scope simulator data," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 12-22.
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    Citations

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    Cited by:

    1. Park, Jinkyun, 2024. "A framework to determine the holistic multiplier of performance shaping factors in human reliability analysis – An explanatory study," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Park, Jooyoung & Boring, Ronald L. & Ulrich, Thomas A. & Lew, Roger & Lee, Sungheon & Park, Bumjun & Kim, Jonghyun, 2022. "A framework to collect human reliability analysis data for nuclear power plants using a simplified simulator and student operators," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Hu, Lunhu & Pan, Xing & Ding, Song & Zuo, Dujun & Kang, Rui, 2022. "A quantitative input for evaluating human error of visual Neglection: Prediction of Operator's detection time spent on perceiving critical visual signal," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. 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).
    6. Kim, Yochan & Park, Jinkyun & Presley, Mary, 2021. "Selecting significant contextual factors and estimating their effects on operator reliability in computer-based control rooms," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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