Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings-Reference-Cited by-同舟云学术

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings

Published:2024-05-17 Issue: Volume: Page:1204-1209
ISSN:
Container-title:2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Wang Li¹,Qu Qilin¹,Wang Yanwei²,Wong David Shan-Hill³,Zheng Ying¹

Affiliation:

1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology,China -Belt and Road Joint Laboratory On Measurement and Control Technology,Wuhan,China,430074

2. School of Mechanical & Electrical Engineering, Wuhan Institute of Technology,Wuhan,430073

3. National Tsing-Hua University,Department of Chemical Engineering,Hsinchu,Taiwan,30071

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606651.pdf?arnumber=10606651

Reference18 articles.

1. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

2. Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

3. A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings

4. Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery

5. Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

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