Imbalanced Fault Diagnosis of Rolling Bearing Using Data Synthesis Based on Multi-Resolution Fusion Generative Adversarial Networks

Imbalanced Fault Diagnosis of Rolling Bearing Using Data Synthesis Based on Multi-Resolution Fusion Generative Adversarial Networks

Fault diagnosis of industrial bearings plays an invaluable role in the health monitoring of rotating machinery. In practice, there is far more normal data than faulty data, so the data usually exhibit a highly skewed class distribution. Algorithms developed using unbalanced datasets will suffer from...

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Bibliographic Details
Main Authors:	Du, J. (Author), Hao, C. (Author), Liang, H. (Author)
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	data augmentation feature transfer generative adversarial networks imbalanced fault diagnosis
Online Access:	View Fulltext in Publisher

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