Contrastive Learning Framework With Cross-Sensor Adaptive Signal Representation for Fault Diagnosis

Although multisource sensor (MS) signal-based mechanical fault diagnosis (MFD) can significantly improve the diagnostic performance, the existing methods often lack sufficient adaptability and generalization when retraining on single-sensor signals or inferring from partial sensor signals. Thus, a g...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 36; číslo 10; s. 17801 - 17813
Hlavní autori:	He, Wenbin, Mao, Jianxu, Wang, Yaonan, Li, Zhe, Zhang, Hui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.10.2025
Predmet:	Adaptation models Adaptive signal representation Contrastive learning Fault diagnosis Feature extraction mechanical fault diagnosis (MFD) Monitoring multisource sensor (MS) Robot sensing systems Signal representation supervised contrastive learning Training Vectors Visual perception
ISSN:	2162-237X, 2162-2388, 2162-2388
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