Presentation Details
Fault diagnosis of mechanical equipment using a gradient information-constrained generative adversarial networks
ID：113
View Protection：ATTENDEE
Updated Time：2025-11-10 15:36:58 Hits：110
Poster Presentation
Start Time：Pending（Asia/Shanghai）
Duration：Pending
Session：No Session »
No files
Abstract
The performance of mechanical equipment may decrease due to long-term operation under harsh working conditions, so to ensure the normal operation of the equipment, it is necessary to accurately diagnose the health status of key components. To improve the fault diagnosis performance of mechanical equipment in the case of scarce fault data, a gradient information constrained generative adversarial network is proposed to enhance the fault diagnosis capability by data augmentation. Firstly, the encoder is integrated into the discriminator to extract effective features from the original samples, constructing a generative adversarial network with stronger data synthesis capabilities. By generating fault samples that are similar to real samples, the effectiveness of fault diagnosis under sample scarcity can be improved. Secondly, a gradient information constraint mechanism is constructed based on the information bottleneck theory to improve the training stability of the generative adversarial network. By imposing constraints on the mutual information between the input data and the deep features of the discriminator, the feedback gradient of the discriminator to the generator can be effectively adjusted, further promoting the stable training of the network structure. The experimental verification on the bearing dataset shows that the proposed method has excellent ability in small sample diagnostic tasks.
Keywords
fault diagnosis, mechanical equipment, generative adversarial networks, small sample, gradient information
Speaker
shaowei liu
Overall Technical De Xi'an Modern Control Technology Research InstituteSubmission Author
All Comments
Important Date
-
Conference Date
Nov 21
2025
toNov 23
2025
-
Oct 20 2025
Draft paper submission deadline
-
Dec 08 2025
Registration deadline
Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
South China University of Technology
Organized By
South China University of Technology
Contact Information
Previous Conferences
-
2026-11-13 China Chengdu
2026 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) -
2026-11-06 China Chengdu
2026 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2023-11-02 China China
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2023-11-02 China Xi'an
2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2022-11-30 China Harbin
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2021-10-21 China Nanjing
2021 International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence -
2020-10-15 China Xi'an
International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence
Submit Comment