A review of deep learning-based few sample fault diagnosis method for rotating machinery

A review of deep learning-based few sample fault diagnosis method for rotating machinery

ObjectivesDeep learning has shown great potential in the field of rotating machinery fault diagnosis. Its excellent performance heavily relies on sufficient training samples. However, in practical engineering applications, acquiring sufficient training data is particularly difficult, resulting in po...

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Bibliographic Details
Main Authors: Ke WU, Jun WU, Qiming SHU, Weiming SHEN, Wenbin SONG
Format: Article
Language:English
Published: Editorial Office of Chinese Journal of Ship Research 2025-04-01
Series:Zhongguo Jianchuan Yanjiu
Subjects:
rotating machinery
failure analysis
fault diagnosis
few sample
meta-learning
transfer learning
domain generalization
data augmentation
self-supervised learning
Online Access:http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.04175
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