Home Journals Articles Updates About
CN
image
收藏切换
Inverter Fault Diagnosis Method Based on CGAN-CNN under Sample Imbalance
收藏切换
PDF
Quan SUN1, Fei PENG1, Hongsheng LI1, Xianghai YU1, Guodong SUN2
Journal of Power Supply | 2024, 22(6) : 318 - 326
Less
收藏切换
Journal of Power Supply | 2024, 22(6): 318-326
• Reliability and Diagnostics •
Inverter Fault Diagnosis Method Based on CGAN-CNN under Sample Imbalance
Full
Quan SUN1, Fei PENG1, Hongsheng LI1, Xianghai YU1, Guodong SUN2
Affiliations
  • 1 School of Automation Nanjing Institute of Technology Nanjing 211167 China
  • 2 College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China
Published: 2024-11-30 doi: 10.13234/j.issn.2095-2805.2024.6.318
Outline
收藏切换

The three-phase inverter is an important part of the motor drive system in an electric vehicle (EV). When a fault occurs, the fault sample size will be limited due to the short occurrence time, resulting in sample imbalance. To solve this problem, an inverter fault diagnosis method combining conditional generative adversarial network (CGAN) and convolutional neural network (CNN) is proposed in this paper. First, the phase current is taken as a fault sensitive signal, its frequency-domain characteristics are obtained by fast Fourier transform, and normalized preprocessing is carried out. Then, each sample is labeled and input into the CGAN model for countermeasure training to generate new samples in each fault mode. Finally, the CNN model is used to distinguish various fault modes of inverter. Through experimental research, it is found that the fault diagnosis accuracy based on CGAN-CNN can reach more than 98%, indicating that the proposed sample generation method is better than the traditional Smote and GAN methods. The results in this paper provide theoretical support for the intelligent operation and maintenance of new energy EVs.

Fault diagnosis  /  sample imbalance  /  sample generation  /  conditional generative adversarial network (CGAN)  /  convolutional neural network (CNN)
Quan SUN, Fei PENG, Hongsheng LI, Xianghai YU, Guodong SUN. Inverter Fault Diagnosis Method Based on CGAN-CNN under Sample Imbalance[J]. Journal of Power Supply, 2024 , 22 (6) : 318 -326 . DOI: 10.13234/j.issn.2095-2805.2024.6.318
Funding
Less
  • National Natural Science Foundation of China(61901212)
  • Natural Science Foundation of Jiangsu Higher Education Institutions of China(20KJA510007)
  • Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network(XTCX201909)
Appendix
Less
Year 2024 volume 22 Issue 6
PDF
399
163
Cite this Article
BibTeX
Article Info
doi: 10.13234/j.issn.2095-2805.2024.6.318
  • Receive Date：2021-12-24
  • Online Date：2025-07-19
  • Published：2024-11-30
Article Data
Affiliations
History
  • Received：2021-12-24
  • Revised：2022-03-13
  • Accepted：2022-04-06
Funding
National Natural Science Foundation of China(61901212)
Natural Science Foundation of Jiangsu Higher Education Institutions of China(20KJA510007)
Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network(XTCX201909)
Affiliations
    1 School of Automation Nanjing Institute of Technology Nanjing 211167 China
    2 College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China
References
Share
https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2024.6.318
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House