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Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant steam generator undergoing multiple degradation mechanisms

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  • Hoseyni, Seyed Mojtaba
  • Di Maio, Francesco
  • Zio, Enrico

Abstract

Condition-Based Probabilistic Safety Assessment (CB-PSA) makes use of inspections and monitoring information on Systems, Structures, and Components (SSCs) to update risk quantities. In this paper, we show the benefits of exploiting the condition-based estimates for taking maintenance decisions on a SSC undergoing multiple degradation mechanisms. To develop the method, we make reference to a spontaneous Steam Generator Tube Rupture (SGTR) Accident Scenario in a Nuclear Power Plant (NPP). The SG is susceptible to multiple degradation mechanisms, i.e., Stress Corrosion Cracking (SCC) and pitting. Tube plugging and Water Lancing and Chemical Cleaning (WL-CC) can be performed, before leading to a SGTR accident. Decisions must be taken on the maintenance strategy to perform at each inspection cycle. Results of a case study regarding SGTR show that the decisions based on the risk estimates provided by a CB-PSA approach allow controlling the SGTR risk at minimum maintenance cost.

Suggested Citation

  • Hoseyni, Seyed Mojtaba & Di Maio, Francesco & Zio, Enrico, 2019. "Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant steam generator undergoing multiple degradation mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018310767
    DOI: 10.1016/j.ress.2019.106583
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    References listed on IDEAS

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    1. Yuan, X.-X. & Mao, D. & Pandey, M.D., 2009. "A Bayesian approach to modeling and predicting pitting flaws in steam generator tubes," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1838-1847.
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    4. Liu, Bin & Liang, Zhenglin & Parlikad, Ajith Kumar & Xie, Min & Kuo, Way, 2017. "Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 200-209.
    5. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    6. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
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    Cited by:

    1. Liu, Jiaxin & Yu, Deping & Yang, Taibo & Liu, Caixue & Wang, Guangjin & Liu, Xiaoming, 2023. "Discovering the causes for the change of the vibration characteristics of the core support barrel in PWR nuclear power plants: A combined investigation based on ex-core neutron noise analysis and nume," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Chadha, Mayank & Ramancha, Mukesh K. & Vega, Manuel A. & Conte, Joel P. & Todd, Michael D., 2023. "The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2020. "Joint optimization of safety barriers for enhancing business continuity of nuclear power plants against steam generator tube ruptures accidents," Reliability Engineering and System Safety, Elsevier, vol. 202(C).

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