Bearing fault diagnosis method based on improved DenseNet with small samples

Shihong Song; Jinfeng Liu; Bingqiang Li; Tianlong Qian; Yang Shen; Haibing Ren

doi:10.1117/12.3033211

5 July 2024 Bearing fault diagnosis method based on improved DenseNet with small samples

Shihong Song, Jinfeng Liu, Bingqiang Li, Tianlong Qian, Yang Shen, Haibing Ren

Author Affiliations +

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131840B (2024) https://doi.org/10.1117/12.3033211
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Solving the small sample problem can not only save data acquisition costs, but also enhance the model's generalization ability to unknown data, thereby ensuring the accuracy and reliability of fault diagnosis results. Therefore, this paper proposes a small-sample bearing fault diagnosis method that combines the ECA attention mechanism and the DenseNet network (ECA-DenseNet). First, the two-dimensional DenseNet network and ECA attention mechanism are converted into one-dimensional networks to reduce computational complexity and information loss. Secondly, the size and position of the convolutional layer in the network are improved and adjusted, and the 1D-ECA module is introduced into the DenseNet model. Finally, we use the original signal as the input of ECA-DenseNet. After feature recognition, the network classifier completes fault feature classification. We verified the effect of the proposed model under different working conditions on the laboratory PT500 bearing data set, and compared it with other diagnostic models. Experimental results show that the proposed method has high fault identification accuracy and generalization ability under small sample conditions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shihong Song, Jinfeng Liu, Bingqiang Li, Tianlong Qian, Yang Shen, and Haibing Ren "Bearing fault diagnosis method based on improved DenseNet with small samples", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131840B (5 July 2024); https://doi.org/10.1117/12.3033211

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KEYWORDS

Data modeling

Statistical modeling

Education and training

Performance modeling

Convolution

Data acquisition

Feature extraction

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