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A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

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  • Groth, Katrina M.
  • Smith, Curtis L.
  • Swiler, Laura P.

Abstract

In the past several years, several international organizations have begun to collect data on human performance in nuclear power plant simulators. The data collected provide a valuable opportunity to improve human reliability analysis (HRA), but these improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this paper, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.

Suggested Citation

  • Groth, Katrina M. & Smith, Curtis L. & Swiler, Laura P., 2014. "A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 32-40.
    • Handle: RePEc:eee:reensy:v:128:y:2014:i:c:p:32-40
    DOI: 10.1016/j.ress.2014.03.010
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    References listed on IDEAS

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    5. Martins, Marcelo Ramos & Maturana, Marcos Coelho, 2013. "Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 89-109.
    6. Groth, Katrina M. & Mosleh, Ali, 2012. "A data-informed PIF hierarchy for model-based Human Reliability Analysis," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 154-174.
    7. Kim, Man Cheol & Seong, Poong Hyun & Hollnagel, Erik, 2006. "A probabilistic approach for determining the control mode in CREAM," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 191-199.
    8. Groth, Katrina M. & Swiler, Laura P., 2013. "Bridging the gap between HRA research and HRA practice: A Bayesian network version of SPAR-H," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 33-42.
    9. Dana Kelly & Curtis Smith, 2011. "Bayesian Inference for Probabilistic Risk Assessment," Springer Series in Reliability Engineering, Springer, number 978-1-84996-187-5, March.
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    Cited by:

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    4. Paglioni, Vincent P. & Groth, Katrina M., 2022. "Dependency definitions for quantitative human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Morais, Caroline & Estrada-Lugo, Hector Diego & Tolo, Silvia & Jacques, Tiago & Moura, Raphael & Beer, Michael & Patelli, Edoardo, 2022. "Robust data-driven human reliability analysis using credal networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Garg, Vipul & Vinod, Gopika & Kant, Vivek, 2023. "Auto-CREAM: Software application for evaluation of HEP with basic and extended CREAM for PSA studies," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    8. Greco, Salvatore F. & Podofillini, Luca & Dang, Vinh N., 2021. "A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    9. Li, Mei & Liu, Zixian & Li, Xiaopeng & Liu, Yiliu, 2019. "Dynamic risk assessment in healthcare based on Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 327-334.
    10. Kim, Yochan & Park, Jinkyun & Jung, Wondea & Jang, Inseok & Hyun Seong, Poong, 2015. "A statistical approach to estimating effects of performance shaping factors on human error probabilities of soft controls," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 378-387.
    11. Liao, Huafei & Groth, Katrina & Stevens-Adams, Susan, 2015. "Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 159-169.
    12. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2023. "A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Li, Jue & Li, Heng & Wang, Fan & Cheng, Andy S.K. & Yang, Xincong & Wang, Hongwei, 2021. "Proactive analysis of construction equipment operators’ hazard perception error based on cognitive modeling and a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    14. 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).
    15. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    16. Garg, Vipul & Vinod, Gopika & Prasad, Mahendra & Chattopadhyay, J. & Smith, Curtis & Kant, Vivek, 2023. "Human reliability analysis studies from simulator experiments using Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    17. Zwirglmaier, Kilian & Straub, Daniel & Groth, Katrina M., 2017. "Capturing cognitive causal paths in human reliability analysis with Bayesian network models," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 117-129.
    18. 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.
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    20. Liu, Peng & Qiu, Yongping & Hu, Juntao & Tong, Jiejuan & Zhao, Jun & Li, Zhizhong, 2020. "Expert judgments for performance shaping Factors’ multiplier design in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 194(C).

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