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应用Labview直观的图形化界面将采集到的有缺陷的轴承信号转换为数字信号,在labview中调用matlab函数程序.将经验模态分解(EMD)引入到轴承的振动特征信号提取中,再从若干个包括故障的IMF分量中提取能量特征参数以判别故障产生的部位。试验结果表明,经验模态分解的分析方法在判断轴承故障的部位时具有很高的准确性,是一种有效的轴承故障诊断方法。
Application of Labview intuitive graphical interface will be collected from the faulty bearing signal is converted to digital signals, called in the labview Matlab function program.Experimental modal decomposition (EMD) is introduced into the bearing vibration signature signal extraction, and then from a number of Extract energy characteristic parameters from IMF components including fault to identify the fault location. The experimental results show that the method of empirical mode decomposition has a high accuracy in judging the fault location of bearings and is an effective method for bearing fault diagnosis.