(A) quantitative study on the effects of unexpected plant conditions to an operator's diagnosis error

Abstract

Since the performance of the human operator has been considered one of the crucial factors to ensure safe operation of nuclear power plants (NPPs), there have been a lot of efforts in systematic and managerial approaches to reduce human errors, such as developing possible predictable scenarios. Despite all these efforts, it is inevitable that human operators encounter unexpected plant conditions. It brings significant impacts on human information processing and enable wide range of error mechanisms and error types as following reasons: a) leading operators to view a large number of informational cues, b) possibly creating distractions and interruption of situation assessment, and c) causing occurrence of impaired cognitive abilities for human operators if conditions worsen. In contrast to common diagnosis situations for the existing human reliability analysis (HRA) methods in which human operators try to find a satisfactory solution in rule-based route, diagnosis activities in unexpected plant conditions require for human operators to do integrative knowledge-based information processing like reasoning and refinement of information. Moreover, strong emotional reactions such as surprise, embarrassing, and anxiety are involved in response of human operators. Because of such differences, it is required to reflect these situational characteristics of unexpected plant conditions in human reliability analysis. Thus, characteristics of unexpected plant conditions were first analyzed in this study, and influencing factors on diagnosis performance of an individual human operator in unexpected plant conditions were analyzed. Since there is no or superficial consideration of the personality factor in the existing HRA methods which is an important influencing factor in unexpected plant conditions, an experiment was conducted to quantify effects of the personality factor on diagnosis performance through an experiment using a compact nuclear simulator. This study identified main influencing factors on diagnosis performance in unexpected plant conditions, and suggested a cognitive model to express cognitive process and causal relationship between influencing factors and diagnosis performance. It is expected to estimate a more precise diagnosis error probability in unexpected plant conditions by applying the derived multipliers of the personality factor through a diagnosis experiment. In addition, it can be a meaningful starting point to research personality traits as a performance predictor for human operators, which are used only to screen out potential mental illness employees in NPPs, by proving the correlation between personality traits and diagnosis performance of human operators, and it is also expected to be a useful reference for further related researches.