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Dynamic safety assessment: Scenario identification via a possibilistic clustering approach

Author

Listed:
  • Podofillini, L.
  • Zio, E.
  • Mercurio, D.
  • Dang, V.N.

Abstract

Dynamic safety and reliability methodologies aim at quantitatively describing the integrated dynamic response of the systems/components/operating crew during an accident by combining the models of the underlying process dynamics and human operator actions with the stochastic processes governing the failure, repair and state transitions of components and systems. The amount of information produced by such dynamic analyses, in terms of scenarios and probability distributions of the occurring events, is very broad and usually calls for a significant effort in the post-processing phase.

Suggested Citation

  • Podofillini, L. & Zio, E. & Mercurio, D. & Dang, V.N., 2010. "Dynamic safety assessment: Scenario identification via a possibilistic clustering approach," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 534-549.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:5:p:534-549
    DOI: 10.1016/j.ress.2010.01.004
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    References listed on IDEAS

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    1. M Kloos & J Peschke, 2008. "Consideration of human actions in combination with the probabilistic dynamics method Monte Carlo dynamic event tree," Journal of Risk and Reliability, , vol. 222(3), pages 303-313, September.
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    Cited by:

    1. Zheng, Xiaoyu & Tamaki, Hitoshi & Sugiyama, Tomoyuki & Maruyama, Yu, 2022. "Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. Tao, Tao & Zio, Enrico & Zhao, Wei, 2018. "A novel support vector regression method for online reliability prediction under multi-state varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 35-49.
    4. Xing Pan & Lunhu Hu & Ziling Xin & Shenghan Zhou & Yanmei Lin & Yong Wu, 2018. "Risk Scenario Generation Based on Importance Measure Analysis," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    5. Park, Jong Woo & Lee, Seung Jun, 2022. "Simulation optimization framework for dynamic probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    6. Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi, 2018. "A systematic framework of vulnerability analysis of a natural gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 79-91.
    7. Lin, Yufei & Chen, Maoyin & Zhou, Donghua, 2013. "Online probabilistic operational safety assessment of multi-mode engineering systems using Bayesian methods," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 150-157.
    8. Podofillini, L. & Dang, V.N., 2012. "Conventional and dynamic safety analysis: Comparison on a chemical batch reactor," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 146-159.
    9. Jing Xiao & Qiongqiong Xu & Chuanli Wu & Yuexia Gao & Tianqi Hua & Chenwu Xu, 2016. "Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
    10. 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.

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