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
denoising autoencoders
deep learning
signal processing
convolutional neural networks
clustering trained representations
nuclear reactors
unfolding
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
Proceedings of the International Joint Conference on Neural Networks
Vol. 2018-July 8489130
978-150906014-6 (ISBN)
Rio de Janeiro, Brazil,
Core monitoring techniques and experimental validation and demonstration (CORTEX)
Europeiska kommissionen (EU) (EC/H2020/754316), 2017-09-01 -- 2021-08-31.
Ämneskategorier
Data- och informationsvetenskap
Annan fysik
Styrkeområden
Energi
DOI
10.1109/IJCNN.2018.8489130