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Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

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  • Bao, Han
  • Zhang, Hongbin
  • Shorthill, Tate
  • Chen, Edward
  • Lawrence, Svetlana

Abstract

Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

Suggested Citation

  • Bao, Han & Zhang, Hongbin & Shorthill, Tate & Chen, Edward & Lawrence, Svetlana, 2023. "Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005889
    DOI: 10.1016/j.ress.2022.108973
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    References listed on IDEAS

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    1. Zhou, Taotao & Droguett, Enrique López & Modarres, Mohammad, 2020. "A common cause failure model for components under age-related degradation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    2. O’Connor, Andrew & Mosleh, Ali, 2016. "A general cause based methodology for analysis of common cause and dependent failures in system risk and reliability assessments," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 341-350.
    3. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Qi, Meng & Kan, Yufeng & Li, Xun & Wang, Xiaoying & Zhao, Dongfeng & Moon, Il, 2020. "Spurious activation and operational integrity evaluation of redundant safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    5. Nguyen, H.D. & Gouno, E., 2020. "Bayesian inference for Common cause failure rate based on causal inference with missing data," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    6. Guo, Yongjin & Zhong, Mingjun & Gao, Chao & Wang, Hongdong & Liang, Xiaofeng & Yi, Hong, 2021. "A discrete-time Bayesian network approach for reliability analysis of dynamic systems with common cause failures," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Wang, Chaonan & Xing, Liudong & Levitin, Gregory, 2014. "Explicit and implicit methods for probabilistic common-cause failure analysis," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 175-184.
    8. Nguyen, H.D. & Gouno, E., 2019. "Maximum likelihood and Bayesian inference for common-cause of failure model," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 56-62.
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