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A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators

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  • Lee, Hyun-Chul
  • Seong, Poong-Hyun

Abstract

Operators in nuclear power plants have to acquire information from human system interfaces (HSIs) and the environment in order to create, update, and confirm their understanding of a plant state, as failures of situation assessment may cause wrong decisions for process control and finally errors of commission in nuclear power plants. A few computational models that can be used to predict and quantify the situation awareness of operators have been suggested. However, these models do not sufficiently consider human characteristics for nuclear power plant operators.

Suggested Citation

  • Lee, Hyun-Chul & Seong, Poong-Hyun, 2009. "A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1796-1805.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:11:p:1796-1805
    DOI: 10.1016/j.ress.2009.05.012
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    References listed on IDEAS

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    1. Kim, Man Cheol & Seong, Poong Hyun, 2006. "A computational model for knowledge-driven monitoring of nuclear power plant operators based on information theory," Reliability Engineering and System Safety, Elsevier, vol. 91(3), pages 283-291.
    2. Chang, Y.H.J. & Mosleh, A., 2007. "Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents," Reliability Engineering and System Safety, Elsevier, vol. 92(8), pages 1041-1060.
    3. Kim, Man Cheol & Seong, Poong Hyun, 2006. "An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 91(3), pages 270-282.
    4. Chang, Y.H.J. & Mosleh, A., 2007. "Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents," Reliability Engineering and System Safety, Elsevier, vol. 92(8), pages 1076-1101.
    5. Chang, Y.H.J. & Mosleh, A., 2007. "Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model," Reliability Engineering and System Safety, Elsevier, vol. 92(8), pages 1014-1040.
    6. Chang, Y.H.J. & Mosleh, A., 2007. "Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 4: IDAC causal model of operator problem-solving response," Reliability Engineering and System Safety, Elsevier, vol. 92(8), pages 1061-1075.
    7. Chang, Y.H.J. & Mosleh, A., 2007. "Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents," Reliability Engineering and System Safety, Elsevier, vol. 92(8), pages 997-1013.
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    Cited by:

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    4. 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).
    5. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    6. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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