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滚动轴承的运行状态会直接影响到整个旋转机械的性能,提出一种将经验模态分解和Hilbert包络分析相结合的方法对滚动轴承进行故障诊断。经验模态分解具有自适应性,能有效地将携带故障信息的高频调制信号从原信号中分离出来;利用Hilbert变换对包含滚动轴承故障所在的高频段进行包络谱分析,提取故障特征频率。将提取的特征频率与根据轴承型号参数和转速所得的滚动轴承的故障特征频率进行对比,能够辨识出滚动轴承的故障。通过对实验采集的滚动轴承振动信号进行分析,证明了该方法有效性和准确性。
The running state of the rolling bearing will directly affect the performance of the whole rotating machine. A fault diagnosis method based on the empirical mode decomposition and the Hilbert envelope analysis is proposed. Empirical mode decomposition is adaptive, which can effectively separate the high-frequency modulated signal carrying fault information from the original signal. Envelope spectrum analysis is performed on the high-frequency range including rolling bearing fault using Hilbert transform, and the fault characteristic frequency is extracted. Comparing the extracted characteristic frequency with the fault characteristic frequency of the rolling bearing based on the bearing type parameters and speed can identify the fault of the rolling bearing. The vibration signal of the rolling bearing collected experimentally is analyzed to prove the effectiveness and accuracy of the method.