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

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

doi:https://doi.org/10.5194/ms-16-431-2025

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

Cited articles

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Cao, H., Sun, P., and Zhao, L.: PCA-SVM method with sliding window for online fault diagnosis of a small pressurized water reactor, Ann. Nucl. Energy, 171, 109036, https://doi.org/10.1016/j.anucene.2022.109036, 2022.

Chen, B., Zhang, W., Gu, J. X., Song, D., Cheng, Y., Zhou, Z., Gu, F., and Ball, A. D.: Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis, Mech. Syst. Signal. Pr., 193, 110270, https://doi.org/10.1016/j.ymssp.2023.110270, 2023.

Cui, L., Liu, Y., Zhao, D., and Zhen, D.: Egram based SVD method for gear fault diagnosis, IEEE Sens. J., 22, 13188–13200, https://doi.org/10.1109/JSEN.2022.3177144, 2022.

Case Western Reserve University: Bearing Data Center, https://engineering.case.edu/bearingdatacenter/download-data-file, last access: 1 March 2023.

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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.