A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors

A challenge to Human Reliability Analysis (HRA) for Nuclear Power Plants (NPPs) lies in the fact that dependencies among Performance Shaping Factors (PSFs) are difficult to deal with due to insufficient knowledge, information and data available. Existing treatment relies heavily on the subjective expert judgment and the dependencies are compromised with the quantities of PSFs, simultaneously, neglects their uncertain interactions. This study proposes a Bayesian Belief Network (BBN) framework for structuring the uncertain dependencies among PSFs and estimate the Human Error Probabilities (HEPs) giving due account to such dependencies. An Exploratory Factor Analysis (EFA) technique is used to analyze human error events and cluster the dependent PSFs into clusters, which serve as the nodes connecting the parent PSF nodes with the child HEP node. Monte Carlo (MC) sampling operationalizes the framework, accounting for the uncertainty that affects PSF clustering and the data filling of conditional probability tables is performed by a Fenton approach. The framework is illustrated by considering 89 human error reports of China NPPs. The results show that the human mental related PSFs complexity, stress/stressor and fitness for duty are highly and steadily dependent, however, the dependencies of the PSFs experience/training and work processes are determined by the specific system situations.