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A Bayesian approach to modeling and predicting pitting flaws in steam generator tubes

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  • Yuan, X.-X.
  • Mao, D.
  • Pandey, M.D.

Abstract

Steam generators in nuclear power plants have experienced varying degrees of under-deposit pitting corrosion. A probabilistic model to accurately predict pitting damage is necessary for effective life-cycle management of steam generators. This paper presents an advanced probabilistic model of pitting corrosion characterizing the inherent randomness of the pitting process and measurement uncertainties of the in-service inspection (ISI) data obtained from eddy current (EC) inspections. A Markov chain Monte Carlo simulation-based Bayesian method, enhanced by a data augmentation technique, is developed for estimating the model parameters. The proposed model is able to predict the actual pit number, the actual pit depth as well as the maximum pit depth, which is the main interest of the pitting corrosion model. The study also reveals the significance of inspection uncertainties in the modeling of pitting flaws using the ISI data: Without considering the probability-of-detection issues and measurement errors, the leakage risk resulted from the pitting corrosion would be under-estimated, despite the fact that the actual pit depth would usually be over-estimated.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:11:p:1838-1847
    DOI: 10.1016/j.ress.2009.06.001
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    Citations

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    Cited by:

    1. 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).
    2. Reuel Smith & Mohammad Modarres & Enrique López Droguett, 2018. "A recursive Bayesian approach to small fatigue crack propagation and detection modeling," Journal of Risk and Reliability, , vol. 232(6), pages 738-753, December.
    3. Qin, H. & Zhou, W. & Zhang, S., 2015. "Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 334-342.
    4. Hermann, Simone & Ickstadt, Katja & Müller, Christine H., 2018. "Bayesian prediction for a jump diffusion process – With application to crack growth in fatigue experiments," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 83-96.
    5. Zhang, Shenwei & Zhou, Wenxing, 2014. "Bayesian dynamic linear model for growth of corrosion defects on energy pipelines," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 24-31.
    6. Mason, Paolo, 2017. "A Bayesian analysis of component life expectancy and its implications on the inspection schedule," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 87-94.
    7. Kaushik Chatterjee & Mohammad Modarres, 2013. "A probabilistic approach for estimating defect size and density considering detection uncertainties and measurement errors," Journal of Risk and Reliability, , vol. 227(1), pages 28-40, February.
    8. Mason, Paolo, 2016. "Approximate Bayesian Computation of the occurrence and size of defects in Advanced Gas-cooled nuclear Reactor boilers," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 21-25.

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