A novel imbalance fault diagnosis method based on data augmentation and hybrid deep learning models

Auteur(s):	Caizi Fan (School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, P. R. China) Yongchao Zhang (School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, P. R. China) Hui Ma (School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, P. R. China) Zeyu Ma (School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, P. R. China) Xunmin Yin (Harbin Marine Boiler and Turbine Research Institute, Harbin, Heilongjiang, P. R. China) Xiaoxu Zhang (Harbin Marine Boiler and Turbine Research Institute, Harbin, Heilongjiang, P. R. China) Songtao Zhao (Harbin Marine Boiler and Turbine Research Institute, Harbin, Heilongjiang, P. R. China)
Médium:	article de revue
Langue(s):	anglais
Publié dans:	Structural Health Monitoring
DOI:	10.1177/14759217241291143

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