Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Dischar...

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Veröffentlicht in:	IEEE access Jg. 8; S. 5438 - 5454
Hauptverfasser:	Zemouri, Ryad, Levesque, Melanie, Amyot, Normand, Hudon, Claude, Kokoko, Olivier, Tahan, Souheil Antoine
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classifiers Computer Science convolutional variational autoencoder Data analysis Data visualization deep neural networks Diagnosis Diagnostic systems Discharge Failure mechanisms feature extraction generative model Hydrogenerators Machine Learning Maintenance engineering model interpretation Partial discharges Stators Training Training data Utilities Visualization model interpretation feature extraction convolutional variational autoencoder Hydrogenerators diagnosis generative model data visualization partial discharges deep neural networks
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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