Rotating Machinery Fault Diagnosis Based on Residual Dense Network with Multi-branch Channel Attention Mechanism
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8878-5_47
Reference6 articles.
1. Wang MH, Lu SD, Hsieh CC et al (2022) Fault detection of wind turbine blades using multi-channel CNN. Sustainability 14(3):1781
2. Feng X, Zhao G, Wang Y, Gao D, Ding H (2023) Fault diagnosis method based on the multi-head attention focusing on data positional information. Measur Control 56(3–4):583–595
3. Wang C, Zhu G, Liu T, Xie Y, Zhang D (2023) A sub-domain adaptive transfer learning base on residual network for bearing fault diagnosis. J Vibr Control 29(1–2):105–117
4. Wang T, Chen C, Dong X, Liu H (2023) A novel method of production line bearing fault diagnosis based on 2D image and cross-domain few-shot learning. Appl Sci 13(3):1809
5. Zhong C, Jiang Y, Wang L, Chen J, Zhou J, Hong T, Zheng F (2023) Improved MLP energy meter fault diagnosis method based on DBN. Electronics 12(4):932
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