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基于EMD与LS-SVM的刀具磨损识别方法

关山 王龙山 聂鹏

关山, 王龙山, 聂鹏等 . 基于EMD与LS-SVM的刀具磨损识别方法[J]. 北京航空航天大学学报, 2011, 37(2): 144-148.
引用本文: 关山, 王龙山, 聂鹏等 . 基于EMD与LS-SVM的刀具磨损识别方法[J]. 北京航空航天大学学报, 2011, 37(2): 144-148.
Guan Shan, Wang Longshan, Nie Penget al. Identification method of tool wear based on empirical mode decomposition and least squares support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(2): 144-148. (in Chinese)
Citation: Guan Shan, Wang Longshan, Nie Penget al. Identification method of tool wear based on empirical mode decomposition and least squares support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(2): 144-148. (in Chinese)

基于EMD与LS-SVM的刀具磨损识别方法

基金项目: 辽宁省教育厅重点实验室资助项目(LS2010117)
详细信息
  • 中图分类号: TH 165.3

Identification method of tool wear based on empirical mode decomposition and least squares support vector machine

  • 摘要: 针对刀具磨损声发射信号的非平稳特征和BP神经网络学习算法收敛速度慢、易陷入局部极小值等问题,提出了基于经验模态分解和最小二乘支持向量机的刀具磨损状态识别方法.首先对声发射信号进行经验模态分解,将其分解为若干个固有模态函数之和,然后分别对每一个固有模态函数进行自回归建模,最后提取每一个自回归模型的系数组成特征向量,特征向量被分为两组,一组用于对最小二乘支持向量机训练,另一组用于识别刀具磨损状态.试验结果表明:该方法能很好地识别刀具磨损状态,与BP神经网络相比具有更高的识别率.

     

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出版历程
  • 收稿日期:  2010-08-03
  • 网络出版日期:  2011-02-28

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