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<p>Rolling bearing vibration signals exhibit nonsmoothness and nonlinearity in complicated noise environments, which results in an erroneous feature frequency in signals and low feature extraction accuracy. Thus, with the rolling bearing vibration signal as the subject, this work uses fuzzy entropy (FE) theory and the variational mode decomposition (VMD) method to examine issues such as low feature information extraction accuracy. Simulation signals are created by examining the bearing failure mechanism and its failure form. The VMD method is selected for analysis, and the minimum FE method is proposed as the basis for determining VMD components and parameters [K, α]. A rolling bearing fault diagnostic experimental platform is built. The self-test signal and the Case Western Reserve University open dataset are subjected to a combination of VMD and sample entropy analysis. Experimental results validate the superiority of the VMD technique with FE optimization.</p>

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Extraction of fault features in rolling bearings via VMD and fuzzy entropy techniques

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Ying You,
  • Zhenghao Zhu,
  • Jian Wang,
  • Guoping Yan,
  • Jun Wang,
  • Youqian Liu,
  • Yufeng You

摘要

Rolling bearing vibration signals exhibit nonsmoothness and nonlinearity in complicated noise environments, which results in an erroneous feature frequency in signals and low feature extraction accuracy. Thus, with the rolling bearing vibration signal as the subject, this work uses fuzzy entropy (FE) theory and the variational mode decomposition (VMD) method to examine issues such as low feature information extraction accuracy. Simulation signals are created by examining the bearing failure mechanism and its failure form. The VMD method is selected for analysis, and the minimum FE method is proposed as the basis for determining VMD components and parameters [K, α]. A rolling bearing fault diagnostic experimental platform is built. The self-test signal and the Case Western Reserve University open dataset are subjected to a combination of VMD and sample entropy analysis. Experimental results validate the superiority of the VMD technique with FE optimization.