med2d

代码说明：

  一种基于最小熵反褶积( ]8+83)3 6+D*(A4 Q20(+W(-)D8(+，]6Q) 的滚动轴承故障特征提取方法: 在利用 /X 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"(Based on minimum entropy deconvolution (] 8+83) 3 6+D* (A4 Q20 (+W (-) D8 (+,] 6Q)' s fault feature extraction method: using/X model to remove the uncertainty generated signal gear on the basis of the retention signal minimum entropy deconvolution, and enhance the impact of signal " This method avoids the problems of traditional bearing fault diagnosis method of the bandpass filter design, real vehicle test showed that: compared with the resonance demodulation technology, which fault feature extraction method is more obvious, more suitable for engineering applications. " )

文件列表：

med2d.m,7976,2014-05-12