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基于改进EMD和LS-SVM的刀具磨损状态识别

Keywords: 刀具磨损,状态识别,经验模态分解

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Abstract:

针对经验模态分解(empiricalmodedecomposition,EMD)的端点效应、停止准则和虚假分量作了改进处理,通过对仿真信号的对比验证证明了改进方法的可行性.采集切削加工中的声发射(acousticemission,AE)信号并对AE信号运用改进EMD方法分解为若干个固有模态函数(intrinsicmodefunction,IMF)分量,利用IMF分量和原始信号的相关系数作为判断依据,剔除分解中产生的虚假分量,然后提取IMF分量的归一化能量值并将其作为特征向量.将提取的特征向量分为2组:一组用于对最小二乘支持向量机(leastsquaressupportvectormachine,LS-SVM)训练;另一组用于识别刀具磨损状态.实验结果表明该方法可有效地表征刀具的磨损状态.

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