A deep learning approach to anomaly detection in nuclear reactors
Paper i proceeding, 2018
illustrate that the origin of perturbations can be localised with high accuracy, despite limited training data and obscured/noisy signals, across various levels of granularity.
anomaly detection
clustering trained representations
denoising autoencoders
deep learning
unfolding
signal processing
nuclear reactors
convolutional neural networks
Författare
Francesco Calivá
University of Lincoln
Fabio De Sousa Ribeiro
University of Lincoln
Antonios Mylonakis
Chalmers, Fysik, Subatomär fysik och plasmafysik
Christophe Demaziere
Chalmers, Fysik, Subatomär fysik och plasmafysik
Paolo Vinai
Chalmers, Fysik, Subatomär fysik och plasmafysik
Georgios Leontidis
University of Lincoln
Stefanos Kollias
University of Lincoln
International Joint Conference on Neural Networks - IJCNN2018
Rio de Janeiro, Brazil,
Core monitoring techniques and experimental validation and demonstration (CORTEX)
Europeiska kommissionen (Horisont 2020), 2017-09-01 -- 2021-08-31.
Ämneskategorier
Data- och informationsvetenskap
Annan fysik
Styrkeområden
Energi
DOI
10.1109/IJCNN.2018.8489130