A multi-scale feature extraction and fusion method for bearing fault diagnosis based on hybrid attention mechanism
Bearing failure is one of the most common failures in rotating mechanical. Therefore, rapid and accurate diagnosis of bearing faults is of great significance for ensuring the reliability of equipment. In recent years, researchers have overlooked the correlation and complementarity between different...
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Veröffentlicht in: | Signal, image and video processing image and video processing, 2024, Vol.18 (Suppl 1), p.31-41 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Bearing failure is one of the most common failures in rotating mechanical. Therefore, rapid and accurate diagnosis of bearing faults is of great significance for ensuring the reliability of equipment. In recent years, researchers have overlooked the correlation and complementarity between different sources of information, which has limited the accuracy and robustness of fault diagnosis. This paper proposes a multi-scale feature extraction and fusion method for bearing fault diagnosis. By extracting features from data at different scales, the method can comprehensively perceive the overall information of the signal and accurately capture bearing fault characteristics. Moreover, an improved CBAM mechanism is introduced to automatically adjust the weights of feature maps, enhancing the discriminability and anti-interference ability of the features. The effectiveness of the proposed method is verified on the Paderborn bearing dataset. The results show that the diagnostic accuracy of the proposed method can reach 99.43%, which is significantly better than other methods. |
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ISSN: | 1863-1703 1863-1711 |
DOI: | 10.1007/s11760-024-03129-w |