Abstract
Anomaly Detection in the nuclear reactor is a crucial technology to prevent malfunctions or unplanned shutdowns and to enhance efficient operations. Despite its importance, it remains at the level of relying on rule-based diagnostic or expert judgment due to the lack of training data. It is challenging to obtain real data from the nuclear reactor because of safety and security issues. To overcome those challenges, this paper proposes a new simulation-based anomaly detection methodology in nuclear reactors. We investigate assignable causes of abnormal behaviors in the nuclear reactor, generate simulation data using nuclear core analysis code, RAST-K, and apply the classification models for detecting abnormal behaviors. The proposed method is validated by the control rod positional anomalies simulation data generated by RAST-K.
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