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Using Expert Models in Human Reliability Analysis—A Dependence Assessment Method Based on Fuzzy Logic

Author

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  • Luca Podofillini
  • Vinh Dang
  • Enrico Zio
  • Piero Baraldi
  • Massimo Librizzi

Abstract

In human reliability analysis (HRA), dependence analysis refers to assessing the influence of the failure of the operators to perform one task on the failure probabilities of subsequent tasks. A commonly used approach is the technique for human error rate prediction (THERP). The assessment of the dependence level in THERP is a highly subjective judgment based on general rules for the influence of five main factors. A frequently used alternative method extends the THERP model with decision trees. Such trees should increase the repeatability of the assessments but they simplify the relationships among the factors and the dependence level. Moreover, the basis for these simplifications and the resulting tree is difficult to trace. The aim of this work is a method for dependence assessment in HRA that captures the rules used by experts to assess dependence levels and incorporates this knowledge into an algorithm and software tool to be used by HRA analysts. A fuzzy expert system (FES) underlies the method. The method and the associated expert elicitation process are demonstrated with a working model. The expert rules are elicited systematically and converted into a traceable, explicit, and computable model. Anchor situations are provided as guidance for the HRA analyst's judgment of the input factors. The expert model and the FES‐based dependence assessment method make the expert rules accessible to the analyst in a usable and repeatable way, with an explicit and traceable basis.

Suggested Citation

  • Luca Podofillini & Vinh Dang & Enrico Zio & Piero Baraldi & Massimo Librizzi, 2010. "Using Expert Models in Human Reliability Analysis—A Dependence Assessment Method Based on Fuzzy Logic," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1277-1297, August.
    • Handle: RePEc:wly:riskan:v:30:y:2010:i:8:p:1277-1297
    DOI: 10.1111/j.1539-6924.2010.01425.x
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    References listed on IDEAS

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    Cited by:

    1. Xiaoyan Su & Sankaran Mahadevan & Peida Xu & Yong Deng, 2015. "Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1296-1316, July.
    2. Peter J. Majewicz & Paul Blessner & Bill Olson & Timothy Blackburn, 2020. "Estimating the Probability of Human Error by Incorporating Component Failure Data from User‐Induced Defects in the Development of Complex Electrical Systems," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 200-214, January.
    3. Wang, Lijing & Wang, Yanlong & Chen, Yingchun & Pan, Xing & Zhang, Wenjin, 2020. "Performance shaping factors dependence assessment through moderating and mediating effect analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Arigi, Awwal Mohammed & Park, Gayoung & Kim, Jonghyun, 2020. "Dependency analysis method for human failure events in multi-unit probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    5. Kim, Yochan & Kim, Jaewhan & Park, Jinkyun, 2023. "A data-informed dependency assessment of human reliability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Patriarca, Riccardo & Ramos, Marilia & Paltrinieri, Nicola & Massaiu, Salvatore & Costantino, Francesco & Di Gravio, Giulio & Boring, Ronald Laurids, 2020. "Human reliability analysis: Exploring the intellectual structure of a research field," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    7. Wang, Lijing & Wang, Yanlong & Chen, Yingchun & Pan, Xing & Zhang, Wenjin & Zhu, Yanzhi, 2020. "Methodology for assessing dependencies between factors influencing airline pilot performance reliability: A case of taxiing tasks," Journal of Air Transport Management, Elsevier, vol. 89(C).
    8. Ji, Changcheng & Gao, Fei & Liu, Wenjiang, 2024. "Dependence assessment in human reliability analysis based on cloud model and best-worst method," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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