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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on automation science and engineering Jg. 22; S. 15757 - 15767
Hauptverfasser:	Zuo, Zhonglin, Meng, Tao, Li, Zheng, Zhang, Hao, Liu, Tong, Chen, Zhansheng, Pang, Zhibo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 2025
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!