Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder

Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results...

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Veröffentlicht in:	Heliyon Jg. 10; H. 9; S. e30670
Hauptverfasser:	Zhang, Lei, Xu, Zhongyang, Lu, Chen, Qiao, Tianjiao, Su, Hongzhi, Luo, Yazhou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 15.05.2024 Elsevier
Schlagworte:	Adversarial neural networks algorithms Chromatographic data chromatography Fault data neural networks oils prediction Sample expansion Transformer Fault data Sample expansion Transformer Chromatographic data Adversarial neural networks
ISSN:	2405-8440, 2405-8440
Online-Zugang:	Volltext
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