A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators

Abstract

Operators in nuclear power plants have to acquire information from human system interfaces (HSIs) and the environment in order to create, update, and confirm their understanding of a plant state, as failures of situation assessment may cause wrong decisions for process control and finally errors of commission in nuclear power plants. A few computational models that can be used to predict and quantify the situation awareness of operators have been suggested. However, these models do not sufficiently consider human characteristics for nuclear power plant operators. In this paper, we propose a computational model for situation assessment of nuclear power plant operators using a Bayesian network. This model incorporates human factors significantly affecting operators’ situation assessment, such as attention, working memory decay, and mental model. As this proposed model provides quantitative results of situation assessment and diagnostic performance, we expect that this model can be used in the design and evaluation of human system interfaces as well as the prediction of situation awareness errors in the human reliability analysis.