Acta Metrologica Sinica  2025, Vol. 46 Issue (2): 222-232    DOI: 10.3969/j.issn.1000-1158.2025.02.10
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Bearing Fault Diagnosis Method Based on Multi-branch Feature Fusion Attention Mechanism
GUO Haiyu1, YU Wei1, ZHANG Xiaoguang2,3,4, LU Fanfan2, CHEN Yang2, ZHAO Xueyi5
1.School of Electrical Engineering, Shenyang University of Technology, Shenyang, Liaoning 110870, China
2.Shanghai Intelligent Quality Technology Co.Ltd, Shanghai 201801, China
3.School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026, China
4.Yangtze Delta Information Intelligence Innovation Research Institute, Wuhu, Anhui 241000, China
5.Anhui Conch Cement Co. Ltd, Wuhu, Anhui 241200, China
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Abstract  A multi-branch multi-scale convolutional neural network with channel attention (MBSACNN) method is proposed to enhance feature extraction and improve accuracy with noises in bearing fault diagnosis.Different from the traditional methods, where only one dimension fault feature was considered, multi-channel multi-input is constructed to extract multi-dimension abundant features and enhance the diversity of sample information from the wavelet transform time-frequency signal, which are combined by the channel attention mechanism.Higher diagnosis accuracy and better noise-robustness are obtained by the MBSACNN compared with the traditional methods, which is verified by Case Western Reserve University (CWRU) bearing dataset and real-world application in cement industry.In the case of a bearing dataset, the accuracy of zero noise and strong noise is 99.99% and 96.97% respectively.Under strong noise,the accuracy of three kinds of cement equipment are all above 97.25%.
Key wordsvibration measurement      bearing fault diagnosis      convolutional neural networks      multi-branch      channel attention mechanism      cement equipment     
Received: 28 May 2024      Published: 04 March 2025
PACS:  TB936  
  TH133.3  
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GUO Haiyu
YU Wei
ZHANG Xiaoguang
LU Fanfan
CHEN Yang
ZHAO Xueyi
Cite this article:   
GUO Haiyu,YU Wei,ZHANG Xiaoguang, et al. Bearing Fault Diagnosis Method Based on Multi-branch Feature Fusion Attention Mechanism[J]. Acta Metrologica Sinica, 2025, 46(2): 222-232.
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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2025.02.10     OR     http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2025/V46/I2/222
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