FTGAN: A Novel GAN-Based Data Augmentation Method Coupled Time–Frequency Domain for Imbalanced Bearing Fault Diagnosis
Author:
Affiliation:
1. School of Information Science and Engineering, Yunnan University, Kunming, China
2. Faculty of Information Engineering and Automation, Kunming University of Science and Technology (KUST), Kunming, China
Funder
Yunnan Provincial Major Science and Technology Special Plan Projects
National Natural Science Foundation of China
Training Plan for Young and Middle-Aged Academic and Technological Leaders in Yunnan Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10012400.pdf?arnumber=10012400
Reference49 articles.
1. Intelligent Fault Diagnosis via Semisupervised Generative Adversarial Nets and Wavelet Transform
2. An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks
3. Conditional GAN and 2-D CNN for Bearing Fault Diagnosis With Small Samples
4. A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis
5. Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty
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