Articles | Volume 16, issue 2
https://doi.org/10.5194/ms-16-685-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
04 Nov 2025
Research article |
| 04 Nov 2025
| 04 Nov 2025
Data dynamic networks coupled with mechanical vibration energy for bearing defect identification
Cited articles
Chen, S. N., Li, L. P., Zhang, S. H., and Yiqing, Z.: EEMD-LSTM-Based Steam Turbine Rotor Rubbing defect identification Model and Its Engineering Application, Journal of Thermal Power Engineering, 38, 159–168, https://doi.org/10.16146/j.cnki.rndlgc.2023.08.020, 2023 (in Chinese with English abstract).
CWRU data: https://engineering.case.edu/bearingdatacenter/download-data-file, last access: 17 January 2025.
Ding, P., Xu, Y., Qin, P., and Sun, M.: A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples, Applied Intelligence 54, 5306–5316, https://doi.org/10.1007/s10489-024-05429-7, 2024.
Eckmann, J. P., Kamphorst, S. O., and Ruelle, D.: Recurrence Plots of Dynamical Systems, Europhys. Lett., 4, 973–977, https://doi.org/10.1209/0295-5075/4/9/004, 1987.
Jia, S. X., Sun, D. Y., Mao, G., and Li, Y. B.: Cross-Condition defect identification Method of Rotor System Based on Adversarial Entropy, Journal of Mechanical Engineering, 59, 110–120, https://doi.org/10.3901/JME.2023.15.110, 2023 (in Chinese with English abstract).
