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A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis

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  • Greco, Salvatore F.
  • Podofillini, Luca
  • Dang, Vinh N.

Abstract

The models adopted in Human Reliability Analysis (HRA) characterize personnel tasks and performance conditions via categories of task and influencing factors (e.g. task types and Performance Shaping Factors, PSF). These categories cover the variability of the operational tasks and conditions affecting performance, and of the associated Human Error Probability (HEP). However, variability exists as well within such categories, for example because of the different scenarios and plants in which data is collected, as well as of the operating crew differences (within-category and crew-to-crew variability). This paper presents a Bayesian model to mathematically aggregate simulator data to estimate failure probabilities, explicitly accounting for the specific tasks, scenarios, plants and crew behavior variability, within a given “constellation†(i.e. combination) of task and factor categories. The general aim of the proposed work is to provide future HRA with reference data with stronger empirical basis for failure probability values, both for their nominal values as well as for their variability and uncertainty. Numerical applications with both artificially-generated data and real simulator data are provided to demonstrate the effects of modelling variability in HEP estimates, to avoid potential overconfidence and biases. The applicability of the proposed model to ongoing simulator data collection programs is also investigated.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:206:y:2021:i:c:s095183202030805x
    DOI: 10.1016/j.ress.2020.107309
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    Citations

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    4. Catelani, Marcantonio & Ciani, Lorenzo & Guidi, Giulia & Patrizi, Gabriele, 2021. "An enhanced SHERPA (E-SHERPA) method for human reliability analysis in railway engineering," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. 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).
    6. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2021. "Analysis of recent operational events involving inappropriate actions: influencing factors and root causes," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. 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).
    8. Asadayoobi, N. & Taghipour, S. & Jaber, M.Y., 2022. "Predicting human reliability based on probabilistic mission completion time using Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Liu, Jianqiao & Zou, Yanhua & Wang, Wei & Zio, Enrico & Yuan, Chengwei & Wang, Taorui & Jiang, Jianjun, 2022. "A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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