Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/ms-16-579-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-16-579-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
21 Oct 2025
Research article |  | 21 Oct 2025

Rub-impact fault diagnosis of rotor systems based on EMD and dynamic CNN

Jinmin Shi, Ruixiao Zhang, and Qian Wang

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Short summary
In this paper, a new rotor rub-impact fault diagnosis method is proposed. The more sensitive fault features associated with the dynamics information are achieved based on the dynamic convolutional neural network. The experiment shows that the proposed method can obtain better diagnosis performance compared with traditional methods and that it has achieved the highest diagnosis accuracy of 98.5 % without noise and 98 % with noise. 
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