Contrastive Conditional Adversarial Autoencoder With Class-Specific Forces for Imbalanced Open-Set Fault Detection

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical ye...

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Vydané v:	IEEE transactions on automation science and engineering Ročník 22; s. 15757 - 15767
Hlavní autori:	Zuo, Zhonglin, Meng, Tao, Li, Zheng, Zhang, Hao, Liu, Tong, Chen, Zhansheng, Pang, Zhibo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	Adaptation models Autoencoders Automation class-specific weighting conditional autoencoder Contrastive learning Data models Fault detection Fault diagnosis Feature extraction imbalanced data Open-set fault detection Representation learning Training
ISSN:	1545-5955, 1558-3783
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