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为提取微弱的轴承故障信号,研究了一种基于最小熵反褶积(Minimum Entropy Deconvolution,MED)的滚动轴承故障特征提取方法:在利用AR模型去除齿轮啮合产生的确定性信号的基础上,对保留信号进行最小熵反褶积,增强冲击信号。该方法避免了传统轴承故障诊断方法中带通滤波器设计的难题,实车测试表明:与共振解调技术相比,该方法提取的滚动轴承故障特征更加明显,更适合于工程应用。
In order to extract weak bearing fault signals, a fault feature extraction method based on Minimum Entropy Deconvolution (MED) for rolling bearing is studied. On the basis of using AR model to remove the deterministic signal generated by gear meshing, The signal is subjected to minimum entropy deconvolution to enhance the impact signal. The method avoids the difficult problem of band-pass filter design in the traditional bearing fault diagnosis method. The actual vehicle test shows that compared with the resonance demodulation technique, the rolling bearing fault characteristic extracted by the method is more obvious and more suitable for engineering applications.