Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 16, issue 2
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.
https://doi.org/10.5194/ms-16-685-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
04 Nov 2025
Research article |  | 04 Nov 2025

Data dynamic networks coupled with mechanical vibration energy for bearing defect identification

Lingwei Hou, Tianfeng Wang, Mukai Wang, Duhui Lu, Sicheng Li, and Faju Qiu

Viewed

Total article views: 215 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
179 25 11 215 14 19
  • HTML: 179
  • PDF: 25
  • XML: 11
  • Total: 215
  • BibTeX: 14
  • EndNote: 19
Views and downloads (calculated since 04 Nov 2025)
Cumulative views and downloads (calculated since 04 Nov 2025)

Viewed (geographical distribution)

Total article views: 212 (including HTML, PDF, and XML) Thereof 212 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Nov 2025
Download
Short summary
In this paper, we propose a data dynamic network for data enhancement for bearing defect identifications. Nodes and edges of data are constructed for the establishment of a data dynamic network. Together with the feature fusion techniques, the energy, recurrence rates, and amplitude–frequency spectra are reconstructed in the time-delayed phase space for preparation of bearing defect identification. The accuracy for bearing defect identification reaches up to 98.5 %.
Read more
Share