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Reliability-based inspection and maintenance planning of a nuclear feeder piping system

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  • Bismut, Elizabeth
  • Pandey, Mahesh D.
  • Straub, Daniel

Abstract

Inspection and maintenance (I&M) is essential to ensure the integrity of feeder pipes, which are parts of the primary heat transport system in a nuclear power plant. The pipes are subject to flow accelerated corrosion (FAC), which can compromise the integrity of the piping system and lead to high repair costs. We explore the opportunity for improving I&M strategies while ensuring that the system still maintains an acceptable level of reliability. To this aim, a reliability-based planning framework is proposed, in which every pipe in the system meets the minimum thickness requirement at a specified annual probability. With this planning framework we can a) evaluate the performance of any I&M strategy constrained to a fixed reliability criterion, without requiring this strategy to be specifically designed for such a criterion; and b) find an I&M strategy optimized for this reliability level using a heuristic description of the strategy space. We demonstrate the framework with a case study, where the wall thinning due to FAC is modeled as a Gamma process with uncertain parameters. We compare the expected life-cycle cost of multiple strategies for I&M of a feeder system with 480 pipes. The proposed approach is compared with an I&M strategy currently used by the industry, which highlights the efficiency of the proposed optimization method.

Suggested Citation

  • Bismut, Elizabeth & Pandey, Mahesh D. & Straub, Daniel, 2022. "Reliability-based inspection and maintenance planning of a nuclear feeder piping system," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022001764
    DOI: 10.1016/j.ress.2022.108521
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    References listed on IDEAS

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    1. Durango, Pablo L. & Madanat, Samer M., 2002. "Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates: an adaptive control approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 763-778, November.
    2. Yuan, Xian-Xun & Higo, Eishiro & Pandey, Mahesh D., 2021. "Estimation of the value of an inspection and maintenance program: A Bayesian gamma process model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Zio, E. & Viadana, G., 2011. "Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1552-1563.
    4. Flage, Roger & Coit, David W. & Luxhøj, James T. & Aven, Terje, 2012. "Safety constraints applied to an adaptive Bayesian condition-based maintenance optimization model," Reliability Engineering and System Safety, Elsevier, vol. 102(C), pages 16-26.
    5. Memarzadeh, Milad & Pozzi, Matteo, 2016. "Value of information in sequential decision making: Component inspection, permanent monitoring and system-level scheduling," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 137-151.
    6. Faddoul, R. & Raphael, W. & Chateauneuf, A., 2018. "Maintenance optimization of series systems subject to reliability constraints," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 179-188.
    7. Wang, Yukun & Li, Xiaopeng & Chen, Junyan & Liu, Yiliu, 2022. "A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Li, Heping & Deloux, Estelle & Dieulle, Laurence, 2016. "A condition-based maintenance policy for multi-component systems with Lévy copulas dependence," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 44-55.
    9. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    10. Bressi, Sara & Santos, João & Losa, Massimo, 2021. "Optimization of maintenance strategies for railway track-bed considering probabilistic degradation models and different reliability levels," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    11. Do, Phuc & Bérenguer, Christophe, 2020. "Conditional reliability-based importance measures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    12. Bismut, Elizabeth & Straub, Daniel, 2021. "Optimal adaptive inspection and maintenance planning for deteriorating structural systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    13. Barone, Giorgio & Frangopol, Dan M., 2014. "Reliability, risk and lifetime distributions as performance indicators for life-cycle maintenance of deteriorating structures," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 21-37.
    14. repec:dau:papers:123456789/1908 is not listed on IDEAS
    15. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    16. Hazra, Indranil & Pandey, Mahesh D. & Manzana, Noldainerick, 2020. "Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    3. Mocellin, Paolo & Pilenghi, Lisa, 2023. "Semi-quantitative approach to prioritize risk in industrial chemical plants aggregating safety, economics and ageing: A case study," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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    6. Lilli, Giordano & Sanavia, Matteo & Oboe, Roberto & Vianello, Chiara & Manzolaro, Mattia & De Ruvo, Pasquale Luca & Andrighetto, Alberto, 2024. "A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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