Presentation Open Access

Deep Learning-based Anomaly Detection in Nuclear Reactor Cores

Thanos Tasakos; George Ioannou; Vasudha Verma; Georgios Alexandridis; Abdelhamid Dokhane; Andreas Stafylopatis

•The introduction of a deep learning methodology for the classification of different perturbation types and their position in the reactor core, using convolutional neural networks
•The performance of a complementary robustness analysis to assess the system's performance on noisy or missing data
•The assessment of the system's functionality on plant measurements obtained from the Gösgennuclear power plan in Switzerland

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