滚动轴承故障特征提取的EMD频谱自相关方法
Feature Extracting Method in the Rolling Element Bearing Fault Diagnosis Based on EMD and Spectrum Auto-correlation

首先，在论述频谱自相关方法(spectrum auto-correlation，简称SAC)的特点、经验模态分解(empirical mode decomposition，简称EMD)分析过程和轴承故障机理的基础上，指出了在故障信号不占主导作用时频谱自相关方法在轴承故障诊断中的局限性，并得到仿真算例验证；然后，提出了基于经验模态分解和频谱自相关的轴承故障特征提取方法，将经验模态分解得到的各分量进行分析比较，再对适合的分量进行频谱自相关分析，可有效提出轴承故障频率；最后，分别在轴承故障试验台实测了深沟球轴承和圆柱滚子轴承内外圈故障振动数据，结果表明,EMD频谱自相关分析方法可以很好地提取轴承故障信号，较单一EMD分解、频谱自相关和峭度等方法效果更好，为轴承故障诊断提供了新思路。
This paper pointed out and verified the limitations of the spectrum auto-correlation (SAC) method based on the analysis process of empirical mode decomposition (EMD) and mechanism of bearing fault. Then, a feature extracting method of bearing fault diagnosis combined with EMD and SAC (EMD-SAC) was proposed, which analyzed the intrinsic mode function (IMF) based on EMD and chose the appropriate component to analyze using SAC. With this method, the bearing failure frequency could be separated from the complex signal. Finally, the inner and outer ring fault of deep groove ball bearings and cylindrical roller bearings respectively were tested in laboratory. The fault vibration data was measured and analyzed to verify the theoretical analysis presented above.