Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

• Published in: Measurement : journal of the International Measurement Confederation Vol. 152; p. 107377
• Main Authors: Zhang, Wei, Li, Xiang, Jia, Xiao-Dong, Ma, Hui, Luo, Zhong, Li, Xu
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.02.2020; Elsevier Science Ltd
• Subjects: Bearings; Datasets; Deep learning; Fault diagnosis; Generative adversarial networks; Imbalanced data; Machine learning; Machinery; Mapping; Noise; Rotating machinery; Rotating machines; Rotation; Signal processing; Training; Vibration; Fault diagnosis; Deep learning; Imbalanced data; Generative adversarial networks; Rotating machines

•A deep learning method is proposed to address the data imbalance problem.•Deep generative adversarial networks are designed to generate fake samples.•Fake samples are similar with real machinery vibration data.•Experiments validate the proposed method on data augmentation in diagnosis. Despite the...