A multi-scale feature extraction and fusion method for bearing fault diagnosis based on hybrid attention mechanism-Reference-Cited by-同舟云学术

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A multi-scale feature extraction and fusion method for bearing fault diagnosis based on hybrid attention mechanism

Published:2024-04-06 Issue:S1 Volume:18 Page:31-41
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Meng Huan,Zhang Jiakai,Zhao Jingbo,Wang Daichao

Funder

Qingdao Municipal People's Livelihood Program

Key Technology Research and Development Program of Shandong Province

National Natural Science Foundation of China

Qingdao City Natural Science Foundation

Qingdao Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11760-024-03129-w.pdf

Reference34 articles.

1. Zhao, Z., Wu, S., Qiao, B., Wang, S., Chen, X.: Enhanced sparse period-group lasso for bearing fault diagnosis. IEEE Trans. Industr. Electron. 66(3), 2143–2153 (2019)

2. Wang, J., Fu, P., Ji, S., Li, Y., Gao, R.X.: A light weight multisensory fusion model for induction motor fault diagnosis. IEEE/ASME Trans. Mechatron. 27(6), 4932–4941 (2022)

3. Shao, H., Jiang, H., Zhang, H., Liang, T.: Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network. IEEE Trans. Industr. Electron. 65(3), 2727–2736 (2018)

4. Tian, Y., Lu, C., Wang, Z.L.: Approach for hydraulic pump fault diagnosis based on wpt-svd and svm. In: Modern Design Technologies and Experiment for Advanced Manufacture and Industry. Applied Mechanics and Materials, 764, 191–197 (2015)

5. Hou, Z.-S., Wang, Z.: From model-based control to data-driven control: Survey, classification and perspective. Inf. Sci. 235, 3–35 (2013)

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