Machine learning for analysis of real nuclear plant data in the frequency domain

Stefanos Kollias,Miao Yu,James Wingate,Aiden Durrant,Georgios Leontidis,Georgios Alexandridis,Andreas Stafylopatis,Antonios Mylonakis,Paolo Vinai,Christophe Demaziere

Annals of Nuclear Energy（2022）

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摘要

•Machine learning is used to effectively detect anomalies in nuclear power plants.•Deep neural networks are trained using simulated data in the frequency domain.•Training data are generated by the CORE SIM+ neutronic modelling tool.•Domain adaptation methods are used to align simulated and real nuclear plant data.•Unsupervised and self-supervised methods detect anomalies’ types and locations.

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关键词

Neutron noise,Machine learning,Domain adaptation,Unsupervised learning,Clustering,Self-supervised learning,Core diagnostics,Core monitoring,Simulated data,Actual plant data

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