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Volume 51 Issue 4
Apr.  2025
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Journal of Beijing University of Aeronautics and Astronautics  > 2025  >  51(4): 1152-1162.
LEI C L，JIAO M X，FAN G F，et al. Fault diagnosis method of rolling bearings based on SSA-IWT-EMD[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（4）：1152-1162 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0174
Citation: LEI C L，JIAO M X，FAN G F，et al. Fault diagnosis method of rolling bearings based on SSA-IWT-EMD[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（4）：1152-1162 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0174
shu

Fault diagnosis method of rolling bearings based on SSA-IWT-EMD

doi: 10.13700/j.bh.1001-5965.2023.0174
  • 1.

    School of Mechanical and Electrical Engineering，Lanzhou University of Technology，Lanzhou 730050，China

  • 2.

    Ineos Sinopec Tianjin Petrochemicals Limited，Tianjin 300280，China

  • 3.

    Yunnan Wenshan Aluminum Co.，Ltd.，Wenshan 663000，China

Funds:

National Natural Science Foundation of China (51465035); Natural Science Foundation of Gansu Province, China (20JR5RA466); Hongliu First-class Disciplines Development Program of Lanzhou University of Technology 

More Information
  • Corresponding author: E-mail：li_jh@vip.sina.com
  • Received Date: 12 Apr 2023
  • Accepted Date: 30 Jun 2023
    • Available Online: 14 Jul 2023
  • Publish Date: 10 Jul 2023
    Abstract

  • Wavelet threshold denoising is insufficient, and feature frequency extraction of empirical mode decomposition (EMD) is unclear. To address these issues, a fault diagnosis method of rolling bearings based on sparrow search algorithm - improved wavelet threshold-EMD (SSA-IWT-EMD) was proposed. Firstly, two adjustment factors were introduced, and an IWT function was presented to overcome the shortcomings of traditional soft and hard thresholds. The SSA was used to globally optimize the parameters of the IWT to reduce the noise of rolling bearing signals. Secondly, a comprehensive index P was put forward to select and reconstruct the components generated by EMD, so as to highlight the fault feature information of the signals. Finally, the fault diagnosis of bearings was realized by envelope spectrum analysis. The simulation and experimental results verified the effectiveness of the proposed method. At the same time, the comparison with the single index component selection method and the literature method indicated that the comprehensive index P and the method proposed in this paper had stronger denoising ability and feature extraction ability, and the envelope spectrum amplitude and frequency doubling component were more obvious, which could better realize the fault diagnosis of rolling bearings.

     

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