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Fault diagnosis method for lightweight gearboxes based on depth-separable cascaded residual block and feature-weighted module

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DOI: 10.23977/jeis.2024.090207 | Downloads: 12 | Views: 201


Jiangfu Liu 1, Jianchao Zhang 1, Yihui Mo 2


1 Beijing Research Institute of Automation for Machinery Industry Co., Ltd, Beijing, China
2 SINOPEC Maoming Petrochemical Company, Maoming, China

Corresponding Author

Jianchao Zhang


Aiming at the problem of insufficient feature extraction in some deep learning-based gearbox fault diagnosis models under small sample conditions leading to lower fault diagnosis accuracy and larger number of parameters, in this paper, a lightweight gearbox fault diagnosis method based on depth-separable cascade residual block and feature weighting module is proposed. Firstly, the one-dimensional original signal of the gearbox is used as the input of this model, which reduces the loss of information in data processing. Then the depth-separable cascade residual block is constructed, which utilizes the depth-separable convolution with a cascade residual structure to maximize the extraction of fault information while reducing the amount of feature parameters. Finally, the feature weighting module strengthened the model's identification and exploitation of key features by calculating the contribution of each channel and giving them weighting. The experimental validation is given by the gearbox dataset of Southeast University, and the experimental results show that the proposed method achieves 99.99% fault diagnosis accuracy under the original signal, and 99.60% under the SNR=6dB noise environment, which shows that the proposed method has high fault diagnosis accuracy and low complexity under the small sample condition.


Fault diagnosis; gearbox; deeply separable convolution; feature weighting module


Jiangfu Liu, Jianchao Zhang, Yihui Mo, Fault diagnosis method for lightweight gearboxes based on depth-separable cascaded residual block and feature-weighted module. Journal of Electronics and Information Science (2024) Vol. 9: 53-67. DOI:


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