Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 94, Issue 11, Pages 1838-1847Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2009.06.001
Keywords
Stochastic deterioration modeling; Pitting corrosion; Bayesian modeling; Markov chain Monte Carlo simulation; Risk-based life-cycle management
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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. (C) 2009 Elsevier Ltd. All rights reserved.
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