EC-WGAN: Enhanced Conditional and Wasserstein GAN for Fault Samples Augmentation
Author:
Affiliation:
1. College of Information Engineering, Zhejiang University of Technology,Hangzhou,China
2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,Nanjing,China
Funder
Natural Science Foundation of Zhejiang Province
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10398736/10398580/10398779.pdf?arnumber=10398779
Reference13 articles.
1. Bearing fault diagnosis method based on data augmentation and MCNN-LSTM
2. Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
3. Data augmentation strategy for power inverter fault diagnosis based on wasserstein distance and auxiliary classification generative adversarial network
4. A novel two-phase clustering-based under-sampling method for imbalanced classification problems
5. Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique
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