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<p>Under non-stationary conditions, the vibration monitoring of CNC is carried out in complex and variable environments. Therefore, low fault diagnosis accuracy results from complicated noise readily overpowering the vibration monitoring signal of the main shaft bearing. To reduce noise and improve diagnosis accuracy, a fault diagnosis technology of machine tool main shaft bearing based on improved FMD is proposed. Firstly, the mode number and filter size of two key FMD parameters are adaptively calculated by ETO optimization technique with weighted envelope spectral kurtosis as fitness function. Then, ideal parameters are obtained by weighted envelope spectral kurtosis, and the largest mode component in envelope spectral analysis is selected according to the optimized FMD parameters. The operational condition of the bearing is diagnosed through the extraction of its fault characteristic frequencies, harmonic components, and modulation features across various bearing configurations. Experimental results show that the proposed method can effectively suppress most of the signal noise.</p>

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Research on feature extraction method of main shaft bearing for CNC machine tools based on improved FMD

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Di Xu,
  • Kai Song,
  • Yaping Wang,
  • Fenyu Gu,
  • Zhongxun Wang,
  • Fangyi Li

摘要

Under non-stationary conditions, the vibration monitoring of CNC is carried out in complex and variable environments. Therefore, low fault diagnosis accuracy results from complicated noise readily overpowering the vibration monitoring signal of the main shaft bearing. To reduce noise and improve diagnosis accuracy, a fault diagnosis technology of machine tool main shaft bearing based on improved FMD is proposed. Firstly, the mode number and filter size of two key FMD parameters are adaptively calculated by ETO optimization technique with weighted envelope spectral kurtosis as fitness function. Then, ideal parameters are obtained by weighted envelope spectral kurtosis, and the largest mode component in envelope spectral analysis is selected according to the optimized FMD parameters. The operational condition of the bearing is diagnosed through the extraction of its fault characteristic frequencies, harmonic components, and modulation features across various bearing configurations. Experimental results show that the proposed method can effectively suppress most of the signal noise.