Abstract Details
203 / 2025-06-14 09:41:08
Research on Motor Fault Diagnosis Based on Improved Convolutional Neural Network of Random Forest under Small Sample Conditions
small sample,CNN,random forest,fault diagnosis
Final Paper
In response to the limitations of traditional convolutional neural networks in motor condition monitoring under small sample conditions, this study proposes an improved convolutional neural network model based on random forests（CNN-RF）, which utilizes Random Forests to replace the softmax classification layer of the original CNN, thereby integrating Random Forest methods with Convolutional Neural Networks. Utilizing the bearing dataset from Huazhong University of Science and Technology, the research generated image sets under small-sample conditions using continuous wavelet transform. The proposed model demonstrates robust classification performance in data-scarce scenarios, achieving 97.38% accuracy with only 10% of the data.
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
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2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
