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Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

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  • Liao, Huafei
  • Groth, Katrina
  • Stevens-Adams, Susan

Abstract

This article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. These challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:144:y:2015:i:c:p:159-169
    DOI: 10.1016/j.ress.2015.07.018
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    References listed on IDEAS

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    1. Preischl, Wolfgang & Hellmich, Mario, 2013. "Human error probabilities from operational experience of German nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 150-159.
    2. James Chang, Y. & Bley, Dennis & Criscione, Lawrence & Kirwan, Barry & Mosleh, Ali & Madary, Todd & Nowell, Rodney & Richards, Robert & Roth, Emilie M. & Sieben, Scott & Zoulis, Antonios, 2014. "The SACADA database for human reliability and human performance," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 117-133.
    3. Park, Jinkyun & Jung, Wondea, 2007. "OPERA—a human performance database under simulated emergencies of nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 503-519.
    4. Podofillini, L. & Dang, V.N., 2013. "A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 52-64.
    5. 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.
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    Cited by:

    1. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2016. "Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 93-112.
    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. Liao, Huafei & Forester, John & Dang, Vinh N. & Bye, Andreas & Chang, Yung Hsien J. & Lois, Erasmia, 2019. "Assessment of HRA method predictions against operating crew performance: Part III: Conclusions and achievements," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. 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).

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