A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors. (December 2022)

• Record Type: Journal Article
• Authors: Liu, Jianqiao; Zou, Yanhua; Wang, Wei; Zio, Enrico; Yuan, Chengwei; Wang, Taorui; Jiang, Jianjun
• Is Part Of: Reliability engineering & system safety. Volume 228(2022)
• Publication Date: 2022-12
• Subjects: Human reliability analysis -- Nuclear power plant -- Performance shaping factor -- Dependency -- Bayesian belief network -- Uncertainty -- Monte Carlo; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108766
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A novel HRA framework is proposed to explore the uncertain dependencies among PSFs. The EFA technique analyzes practical human error reports and cluster the PSFs into causal clusters. The BBN characterizes the dependencies among PSFs. MC sampling operationalizes the proposed framework accounting for the uncertainty that affects the PSFs interactions. Analysis of practical human error reports demonstrates its feasibility. Abstract: 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.