Bayesian Analysis of Piping Failure Frequency Using OECD/NEA Data

The estimation of piping failure frequency is an important task to support the probabilistic risk analysis and risk-informed in-service inspection of nuclear power plant systems (NPPs). Although various probabilistic models have been proposed in the literature, this paper describes a hierarchical or two-stage Poisson-gamma Bayesian procedure to analyze this problem. In the first stage, a generic distribution of failure rate is developed based on the failure observations from a group of similar plants. This distribution represents the interplant (plant-to-plant) variability arising from differences in construction, operation and maintenance conditions. In the second stage, the generic prior obtained from the first stage is updated by using the data specific to a particular plant, and thus a posterior distribution of plan specific failure rate is derived. The two-stage Bayesian procedure is able to incorporate different levels of variability in a more consistent manner. The proposed approach is applied to estimate the failure frequency using the OECD/NEA pipe leakage data for the U.S. nuclear plants.