Generalized Simulation-Based Domain Adaptation Approach for Intelligent Bearing Fault Diagnosis
Author:
Funder
Trường Đại học Bách Khoa Hà Nội
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13369-024-09282-1.pdf
Reference57 articles.
1. Thuan, N.D.; Hue, N. T.; Vuong, P. Q.; Hong, H. S. Intelligent bearing fault diagnosis with a lightweight neural network, In: (2022) 11th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE, pp. 261–266. (2022) https://doi.org/10.1109/ICCAIS56082.2022.9990211.
2. Zhang, X.; Zhao, B.; Lin, Y.: Machine learning based bearing fault diagnosis using the case western reserve university data: a review. IEEE Access 9, 155598–155608 (2021). https://doi.org/10.1109/ACCESS.2021.3128669
3. Ji, M.; Peng, G.; Li, S.; Cheng, F.; Chen, Z.; Li, Z.; Du, H.: A neural network compression method based on knowledge-distillation and parameter quantization for the bearing fault diagnosis. Appl. Soft Comput. 127, 109331 (2022). https://doi.org/10.1016/J.ASOC.2022.109331
4. Chen, X.; Yang, R.; Xue, Y.; Huang, M.; Ferrero, R.; Wang, Z.: Deep transfer learning for bearing fault diagnosis: a systematic review since 2016. IEEE Trans. Instrum. Meas. 72, 1 (2023). https://doi.org/10.1109/TIM.2023.3244237
5. Ruan, D.; Wang, J.; Yan, J.; Gühmann, C.: CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv. Eng. Inform. 55, 101877 (2023). https://doi.org/10.1016/J.AEI.2023.101877
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