NUREG Handbook of Parameter Estimation for Probabilistic Risk Assessment

1.1 Objective

The data analysis portion of a nuclear power plant probabilistic risk assessment (PRA) provides estimates of the parameters used to determine the frequencies and probabilities of the various events modeled in a PRA. The objective of this handbook is to provide methods for estimating the parameters used in PRA models and for quantifying the uncertainties in the estimates.

1.2 Background

Probabilistic risk assessment is a mature technology that can provide a quantitative assessment of the risk from accidents in nuclear powerplants. It involves the development of models that delineate the response of systems and operators to accident initiating events. Additional models are generated to identify the component failure modes required to cause the accident mitigating systems to fail. Each component failure mode is represented as an individual "basic event" in the systems models. Estimates of risk are obtained by propagating the uncertainty distributions for each of the parameters through the PRA models. During the last several years, both the U.S. Nuclear Regulatory Commission (NRC) and the nuclear industry have recognized that PRA has evolved to the point where it can be used in a variety of applications, including as a tool in the regulatory decision-making process. The increased use of PRA has led to the conclusion that the PRA scope and model must be commensurate with the applications. Several procedural guides and standards have been and are being developed that identify requirements for the PRA models. This handbook was generated to supplement these documents. It provides a compendium of good practices that a PRA analyst can use to generate the parameter distributions required for quantifying PRA models. The increased use of risk assessment has also helped promote the idea that the collection and analysis of event data is an important activity in and of itself. In particular, the monitoring of equipment performance and evaluation of equipment trends can be used to enhance plant performance and reliability. The guidance provided in this handbook can support those efforts.