Markov models for evaluating risk-informed in-service inspection strategies for nuclear power plant piping systems

As part of an EPRI sponsored research project to develop technology for risk informed in-service inspection evaluations, new methods and databases were developed to predict piping system reliability. The methods include a Markov modeling technique for predicting the influence of alternative inspection strategies on piping system reliability, and Bayes' uncertainty analysis methods for quantifying uncertainties in piping system reliability parameters. This article describes these methods and associated databases needed for their quantification with particular emphasis on the application of the Markov piping reliability model. Insights are developed regarding reliability metrics that should be used in Probabilistic Risk Assessments for estimating time dependent frequencies of loss of coolant accidents and internal flooding events. The methodology for developing estimates of all the input parameters of the piping reliability models is described including the quantitative treatment of uncertainties in risk informed applications. Examples are presented to demonstrate the practical aspects of applying the Markov model and developing the inputs needed for its quantification.