基于LCD多尺度散布熵的数控机床故障诊断

为提升数控机床用轴承故障诊断效果,提出了基于LCD多尺度散布熵的数控机床轴承故障诊断方法。该方法利用局部特征尺度分解(LCD)对轴承振动信号进行分解,获取原始信号不同尺度下的内禀尺度分量(ISC);根据散布熵能有效区分不同故障信号的复杂度,计算ISC分量的散布熵,获得原始信号多个尺度下的复杂度特征作为轴承故障的特征参数;将该特征参数输入SVM分类器中判断轴承故障,实现故障诊断。轴承不同类型和不同程度故障诊断实验结果表明,所提方法能够提升轴承的故障诊断效果,相比其他一些方法,具有一定的优势。

NC MACHINE FAULT DIGNOSIS BADED ON LCD MULTI DISPERSION ENTROPY

In order to improve fault diagnosis effect of bearing used in NC machine,a fault feature extraction and diagnosis method of bearing based on LCD multi dispersion entropy was proposed.The vibration signal was decomposed adaptively with local characteristic-scale decomposition(LCD) to obtain the components in different scales of the original signal.Considering the ability of the dispersion entropy in distinguishing the complexity of different signals effectively,the dispersion entropy of intrinsic scale components(ISCs) by LCD was calculated.Thus the complexity metric in different scales of the original signal was gained,which was consequently taken as the feature parameter to describe different bearing states.The feature parameters were then put into SVM for diagnosing the bearing faults.Bearing different fault type and different fault degree diagnosis results show that the proposed method can improve diagnosis effect and has certain superiority when compared with some other methods.