Unsupervised Feature-Preserving CycleGAN for Fault Diagnosis of Rolling Bearings Using Unbalanced Infrared Thermal Imaging Sample

The fault diagnosis of rolling bearing is of great significance in industrial safety. The method of infrared thermal image combined with neural network can diagnose the fault of rolling bearing in a non-contact manner, however its data in different scenes are often unbalanced and difficult to obtain...

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Auteurs principaux:	Lujiale Guo, Joon Huang Chuah, Wong Jee Keen Raymond, Xiaohui Gu, Jie Yao, Xiangqian Chang
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2024-01-01
Collection:	IEEE Access
Sujets:	Fault diagnosis rolling bearing infrared thermal imaging unbalanced data generative adversarial networks
Accès en ligne:	https://ieeexplore.ieee.org/document/10433491/
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