| Citation: | QI Xiaoli, MAO Junyi, WANG Zhaojun, et al. Rolling bearing fault diagnosis method based on SConvNeXt-ECMS and DBO-RELM model[J]. Journal of Aerospace Power, 2025, 40（5）：20230678 doi: 10.13224/j.cnki.jasp.20230678
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In view of the existing shortcomings of deep learning-based rolling bearing fault diagnosis methods such as low accuracy and poor generalization, a rolling bearing fault diagnosis model based on SConvNeXt-ECMS （the ConvNeXt network based on shuffled convolution-efficient channel and multi-scale spatial attention module） and DBO-RELM （dung beetle optimizer regularized extreme learning machine） was proposed. The ECMS attention mechanism and shunt convolution module were integrated into the ConvNeXt network to improve the feature extraction capability of the ConvNeXt network. The RELM after parameter optimization by using the dung beetle optimization algorithm was used to replace the original classification layer of the network to improve the network’s resolution of similar features. The aviation bearing fault data set simulation experiment from Harbin Institute of Technology was used to verify the improvement effect of the proposed shunt convolution on the ConvNeXt network. The data set from the University of Paderborn was used to conduct a rolling bearing hybrid fault diagnosis experiment to verify the classification effect of the SConvNeXt-ECMS and DBO-RELM models proposed by the author. Simulation experiment results showed that the proposed SConvNeXt network had an accuracy of up to 100% in the aviation bearing fault classification task, which was better than other existing networks; the rolling bearing hybrid fault diagnosis test showed that the ECMS attention mechanism and the DBO-RELM method had further improved the performance of the original network. The new model can diagnose rolling bearing hybrid faults with a maximum accuracy of 99.94%. Compared with other existing rolling bearing fault diagnosis models, it has higher fault diagnosis accuracy and stronger generalization ability.
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