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

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

Reliability Engineering and System Safety, 2009, vol. 94, issue 11, 1838-1847

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.

Keywords: Stochastic deterioration modeling; Pitting corrosion; Bayesian modeling; Markov chain Monte Carlo simulation; Risk-based life-cycle management (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:94:y:2009:i:11:p:1838-1847

DOI: 10.1016/j.ress.2009.06.001

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