Database development and uncertainty treatment for estimating pipe failure rates and rupture frequencies

Estimates of failure rates for nuclear power plant piping systems are important inputs to Probabilistic Risk Assessments (PRA) and risk informed applications of PRA. Such estimates are needed for initiating event frequencies for Loss of Coolant Accidents and internal flooding events and for risk informed evaluations of piping system in-service inspection programs. A critical issue in the estimation of these parameters is the treatment of uncertainties, which can exceed an order of magnitude deviation from failure rate point estimates. Sources of uncertainty include failure data reporting issues, scarcity of data, poorly characterized component populations, and uncertainties about the physical characteristics of the failure mechanisms and root causes. A methodology for quantifying these uncertainties using a Bayes' uncertainty analysis method was developed for the EPRI risk informed in-service inspection program and significantly enhanced in subsequent applications. In parallel with these efforts, progress has been made in the development of pipe failure databases that contain the quantity and quality of information needed to support piping system reliability evaluations. Examples are used in this paper to identify technical issues with previous published estimates of pipe failure rates and the numerical impacts of these issues on the pipe failure rates and rupture frequencies are quantified.     

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.