A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis

In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern the underlying causes of mechanical faults from a fine-grained perspective, due to the scarcity of annotated data. To tackle those...

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Bibliographic Details
Published in:Neural networks Vol. 178; p. 106482
Main Authors: Zhao, Zhiqian, Xu, Yeyin, Zhang, Jiabin, Zhao, Runchao, Chen, Zhaobo, Jiao, Yinghou
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.10.2024
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ISSN:0893-6080, 1879-2782, 1879-2782
Online Access:Get full text
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