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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Signal, image and video processing image and video processing, 2024, Vol.18 (Suppl 1), p.31-41
Hauptverfasser: Meng, Huan, Zhang, Jiakai, Zhao, Jingbo, Wang, Daichao
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
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.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-024-03129-w