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

[Objectives]Deep 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 poor generalization capability and low diagnostic accuracy. Therefore, few-sample fault diagnosis methods, which can effectively extract …