Vol. 5 No. 1 (2025): Issue 5
Articles

Research on Automotive Bearing Fault Diagnosis Based on the Improved SSA-VMD Algorithm

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  • Weidong Huang,
  • Yiqun Cai
Weidong Huang
Upower Energy Technology (Guang Zhou) Co..Ltd., Guangzhou, 510000, CHINA
Yiqun Cai
Corresponding, University of Florida, Herbert Wertheim College, FL, 32608, USA
DOI: https://doi.org/10.71070/oaml.v5i1.72

Published 2025-02-15

Keywords

  • Automotive Bearing,
  • Improved SSA-VMD Algorithm,
  • Refined Composite Multi-Scale Sample Entropy,
  • Fault Diagnosis

How to Cite

Huang, W., & Cai, Y. (2025). Research on Automotive Bearing Fault Diagnosis Based on the Improved SSA-VMD Algorithm. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.72
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Due to the complexity and variability of automotive bearing fault vibration signals, as well as the challenges in feature extraction, a novel automotive bearing fault diagnosis method based on an improved SSA-VMD algorithm is proposed in this paper. Firstly, the number of modes (K) and the penalty factor (α) in the variational mode decomposition (VMD) algorithm are optimized by the Sparrow Search Algorithm (SSA). Secondly, the VMD using optimized parameters is used to decompose the automotive bearing fault vibration signal into a series of modal components. Modal components with larger kurtosis values are selected for reconstruction and feature extraction. Lastly, the feature vectors are input into a kernel extreme learning machine (KELM) model for fault recognition. A comparative analysis is conducted between the improved SSA-VMD method and the original SSA-VMD method in automotive bearing fault diagnosis. Experimental results demonstrate that the improved SSA-VMD method is satisfactory in extracting characteristic information from automotive bearing fault vibration signals and achieves higher fault diagnosis accuracy.

 

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References

  1. L. Cao, J. Li, Z. Peng, et al.Fault diagnosis of rolling bearing based on EEMD and fast spectral kurtosis[J]. Journal of Mechanical & Engineering,2021, 38: 1311-1316.
  2. M. Li, C. Yan, W. Liu, et al.Fault diagnosis model of rolling bearing based on parameter adaptive AVMD algorithm[J]. Applied Intelligence,2023, 53: 3150-3165.
  3. K. Dragomiretskiy, D. Zosso.Variational mode decomposition[J]. IEEE transactions on signal processing,2013, 62: 531-544.
  4. H. Li, D. Li, H. Zhu, et al.An optimized VMD algorithm and its application in speech signal denoising[J]. Journal of Jilin University,2021, 59: 1219-1227.
  5. G. Huazhan, Z. Ying, S. Kai, et al., in Book Fault Diagnosis of Rolling Bearing Based on SSA-VMD-WPT, ed., ed. by Editor, IEEE, City, 2023, Chap. Chapter, pp. 1-6.
  6. M. Qinghua, Z. Qiang.Improved sparrow algorithm combining Cauchy mutation and reverse learning[J]. Computer Science Exploration,2021, 1-12.
  7. L. Zhang, H. Li, D. Liu, et al.Identification and application of the most suitable entropy model for precipitation complexity measurement[J]. Atmospheric Research,2019, 221: 88-97.
  8. S. Lu, W. Gao, C. Hong, et al.A newly-designed fault diagnostic method for transformers via improved empirical wavelet transform and kernel extreme learning machine[J]. Advanced Engineering Informatics,2021, 49: 101320.
  9. R. Jiao, S. Li, Z. Ding, et al.Fault diagnosis of rolling bearing based on BP neural network with fractional order gradient descent[J]. Journal of Vibration Control,2024, 30: 2139-2153.
  10. J. Wang, Qin.Improved seagull optimization algorithm based on chaotic map and t-distributed mutation strategy[J]. Appl. Res. Comput,2022, 39: 170-176.
  11. X. Li, X. Peng, in Book A new non-destructive method for fault diagnosis of reciprocating compressor by measuring the piston rod strain, ed., ed. by Editor, IOP Publishing, City, 2019, Vol. 604, Chap. Chapter, pp. 012055.
  12. S. Xiao, A. Nie, Z. Zhang, et al.Fault diagnosis of a reciprocating compressor air valve based on deep learning[J]. Applied Sciences,2020, 10: 6596.
  13. L. Zhang, L. Duan, X. Hong, et al., in Book Fault diagnosis method of reciprocating compressor based on domain adaptation under multi-working conditions, ed., ed. by Editor, IEEE, City, 2021, Chap. Chapter, pp. 588-593.
  14. L. ZHANG, L. DUAN, F. WAN.Residual network diagnosis method for reciprocating compressor fault[J]. Journal of Electronic Measurement Instrumentation,2021, 35: 38-46.
  15. S. Gao, Q. Wang, Y. Zhang.Rolling bearing fault diagnosis based on CEEMDAN and refined composite multiscale fuzzy entropy[J]. IEEE Transactions on Instrumentation Measurement,2021, 70: 1-8.
  16. B. Peng, Y. Bi, B. Xue, et al.A survey on fault diagnosis of rolling bearings[J]. Algorithms,2022, 15: 347.
  17. H. Liu, F. Ke, Z. Zhang, et al.Fault Tolerance in Electric Vehicles Using Deep Learning for Intelligent Transportation Systems[J]. Mobile Networks Applications,2023, 1-9.
  18. M. Guo, Y. Huang, Q. Zhao.Fault diagnosis method of rolling bearing based on IFD and KELM[J]. Journal of Fuzhou University,2020, 48: 341-347.