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A method for systematically developing the knowledge base of reactor operators in nuclear power plants to support cognitive modeling of operator performance

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  • Zhao, Yunfei
  • Smidts, Carol

Abstract

Methods based on cognitive modeling have attracted increasing attention in the human reliability analysis community. In such methods, the operator knowledge base plays a central role. This paper proposes a method for systematically developing the knowledge base of nuclear power plant operators. The method starts with a systematic literature review of a predefined topic. Then, the many collected publications are reduced to summaries. Relevant knowledge is then extracted from the summaries using an improved qualitative content analysis method to generate a large number of pieces of knowledge. Lastly, the pieces of knowledge are integrated in a systematic way to generate a knowledge graph consisting of nodes and links. As a case study, the proposed method is applied to develop the knowledge base of reactor operators pertaining to severe accidents in nuclear power plants. The results show that the proposed method exhibits advantages over conventional methods, including reduced reliance on expert knowledge and improved traceability of the process. Generalization of the proposed method to other sources of materials and application of the knowledge base are also discussed. Although this paper is focused on nuclear applications, the proposed method may be extended to other industrial sectors with little additional effort.

Suggested Citation

  • Zhao, Yunfei & Smidts, Carol, 2019. "A method for systematically developing the knowledge base of reactor operators in nuclear power plants to support cognitive modeling of operator performance," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 64-77.
    • Handle: RePEc:eee:reensy:v:186:y:2019:i:c:p:64-77
    DOI: 10.1016/j.ress.2019.02.014
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Yunfei & Smidts, Carol, 2021. "CMS-BN: A cognitive modeling and simulation environment for human performance assessment, part 1 — methodology," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Robinson, Allen C. & Drake, Richard R. & Swan, M. Scot & Bennett, Nichelle L. & Smith, Thomas M. & Hooper, Russell & Laity, George R., 2021. "A software environment for effective reliability management for pulsed power design," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Zheng, Xi & Bolton, Matthew L. & Daly, Christopher & Biltekoff, Elliot, 2020. "The development of a next-generation human reliability analysis: Systems analysis for formal pharmaceutical human reliability (SAFPHâ–ª)," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Xia, Liqiao & Liang, Yongshi & Leng, Jiewu & Zheng, Pai, 2023. "Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. 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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