Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 16, issue 2
Articles | Volume 16, issue 2
https://doi.org/10.5194/ms-16-431-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ms-16-431-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 2
Research article
 | 
09 Sep 2025
Research article |  | 09 Sep 2025

Bearing fault diagnosis method based on an improved particle swarm optimization–variational mode decomposition (PSO–VMD) multidimensional index and its application in a crane amplitude-changing mechanism

Zhao Tian, Zeyang Ke, Hanzhong Liu, Bin Zhao, XiaoChun Zhu, Yudong Tang, and Chuan Zhu

Viewed

Total article views: 455 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
418 30 7 455 31 34
  • HTML: 418
  • PDF: 30
  • XML: 7
  • Total: 455
  • BibTeX: 31
  • EndNote: 34
Views and downloads (calculated since 09 Sep 2025)
Cumulative views and downloads (calculated since 09 Sep 2025)

Viewed (geographical distribution)

Total article views: 439 (including HTML, PDF, and XML) Thereof 439 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Oct 2025
Download
Short summary
In this paper, kurtosis-based improved particle swarm optimization (IPSO) is proposed to optimize the number of penalty factors and decomposition levels in variational mode decomposition (VMD). A method for fusing time domain metrics with entropy features and extracting their streamline features by principal component analysis (PCA) is also proposed. Experimental data validation shows that the method not only reduces the training time of the classification model but also improves the classification accuracy.
Read more
Share