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Development of measures to estimate truncation error in fault tree analysis

Author

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  • Jung, Woo Sik
  • Yang, Joon-Eon
  • Ha, Jaejoo

Abstract

The fault tree quantification uncertainty from the truncation error has been of great concern for the reliability evaluation of large fault trees in the probabilistic safety analysis (PSA) of nuclear plants. The truncation limit is used to truncate cut sets of the gates when quantifying the fault trees. This paper presents measures to estimate the probability of the truncated cut sets, that is, the amount of truncation error. The functions to calculate the measures are programmed into the new fault tree quantifier FTREX (Fault Tree Reliability Evaluation eXpert) and a Benchmark test was performed to demonstrate the efficiency of the measures.

Suggested Citation

  • Jung, Woo Sik & Yang, Joon-Eon & Ha, Jaejoo, 2005. "Development of measures to estimate truncation error in fault tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 30-36.
    • Handle: RePEc:eee:reensy:v:90:y:2005:i:1:p:30-36
    DOI: 10.1016/j.ress.2004.09.007
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    Citations

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    Cited by:

    1. Jung, Woo Sik, 2015. "A method to improve cutset probability calculation in probabilistic safety assessment of nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 134-142.
    2. Park, Jinkyun & Jung, Wondea, 2015. "A systematic framework to investigate the coverage of abnormal operating procedures in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 21-30.
    3. Takeda, Satoshi & Kitada, Takanori, 2023. "Importance measure evaluation based on sensitivity coefficient for probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Duflot, Nicolas & Bérenguer, Christophe & Dieulle, Laurence & Vasseur, Dominique, 2009. "A min cut-set-wise truncation procedure for importance measures computation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1827-1837.
    5. Ibáñez-Llano, Cristina & Rauzy, Antoine & Meléndez, Enrique & Nieto, Francisco, 2010. "A reduction approach to improve the quantification of linked fault trees through binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1314-1323.
    6. Lu, Ji-Min & Wu, Xiao-Yue & Liu, Yiliu & Ann Lundteigen, Mary, 2015. "Reliability analysis of large phased-mission systems with repairable components based on success-state sampling," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 123-133.
    7. Matuzas, V. & Contini, S., 2015. "Dynamic labelling of BDD and ZBDD for efficient non-coherent fault tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 183-192.
    8. Contini, Sergio & Matuzas, Vaidas, 2011. "Analysis of large fault trees based on functional decomposition," Reliability Engineering and System Safety, Elsevier, vol. 96(3), pages 383-390.
    9. Ibáñez-Llano, Cristina & Rauzy, Antoine & Meléndez, Enrique & Nieto, Francisco, 2010. "Hybrid approach for the assessment of PSA models by means of binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1076-1092.
    10. Vaidas Matuzas & Sergio Contini, 2012. "On the efficiency of functional decomposition in fault tree analysis," Journal of Risk and Reliability, , vol. 226(6), pages 635-645, December.
    11. S Contini & V Matuzas, 2012. "Coupling decomposition and truncation for the analysis of complex fault trees," Journal of Risk and Reliability, , vol. 226(3), pages 249-261, June.

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