Conditional contrastive domain generalization for fault diagnosis

Conditional contrastive domain generalization for fault diagnosis

Data-driven fault diagnosis plays a key role in stability and reliability of operations in modern industries. Recently, deep learning has achieved remarkable performance in fault classification tasks. However, in reality, the model can be deployed under highly varying working environments. As a resu...

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Bibliographic Details
Main Authors: Ragab, Mohamed, Chen, Zhenghua, Zhang, Wenyu, Eldele, Emadeldeen, Wu, Min, Kwoh, Chee Keong, Li, Xiaoli
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Contrasting Learning
Domain Generalization
Online Access:https://hdl.handle.net/10356/163780

Internet

https://hdl.handle.net/10356/163780

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