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An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference

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  • Kim, Man Cheol
  • Seong, Poong Hyun

Abstract

Simulation-based human reliability analysis (HRA) methods such as IDAC seem to provide a new direction for the development of advanced HRA methods. In such simulation-based HRA methods, the simulation model for the situation assessment of nuclear power plant (NPP) operators is essential, especially for addressing the issue of errors-of-commission (EOCs). Therefore, we propose an analytic model for the situation assessment of NPP operators based on Bayesian inference. The proposed model is found to be able to address several important features of the situation assessment of NPP operators, and is expected to provide good approximations to some parts of the situation assessment. A comparison with an existing model and identification of several other features of the situation assessment of NPP operators that should be further addressed are also provided.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:reensy:v:91:y:2006:i:3:p:270-282
    DOI: 10.1016/j.ress.2005.01.012
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    Citations

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    Cited by:

    1. 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.
    2. Naderpour, Mohsen & Lu, Jie & Zhang, Guangquan, 2015. "An abnormal situation modeling method to assist operators in safety-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 33-47.
    3. Kim, Man Cheol & Seong, Poong Hyun, 2008. "A method for identifying instrument faults in nuclear power plants possibly leading to wrong situation assessment," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 316-324.
    4. 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.
    5. Lee, Seung Jun & Kim, Man Cheol & Seong, Poong Hyun, 2008. "An analytical approach to quantitative effect estimation of operation advisory system based on human cognitive process using the Bayesian belief network," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 567-577.
    6. Ha, Jun Su & Seong, Poong Hyun, 2009. "A human–machine interface evaluation method: A difficulty evaluation method in information searching (DEMIS)," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1557-1567.
    7. 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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