| 基于EMD与LS-SVM的刀具磨损识别方法 |
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| 引用本文： | 关山,王龙山,聂鹏.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. |
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| 作者姓名： | 关山 王龙山 聂鹏 |
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| 作者单位： | 吉林大学,机械科学与工程学院,长春,130025;沈阳航空航天大学,机电工程学院,沈阳,110136 |
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| 基金项目： | 辽宁省教育厅重点实验室资助项目(LS2010117) |
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| 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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| 关 键 词： | 刀具磨损状态识别 最小二乘支持向量机 经验模态分解 自回归模型 |
| 收稿时间： | 2010-08-03 |
Identification method of tool wear based on empirical mode decomposition and least squares support vector machine |
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| Guan Shan,Wang Longshan,Nie Peng.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. |
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| Authors: | Guan Shan Wang Longshan Nie Peng |
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| Institution: | 1. College of Mechanical Science and Engineering, Jilin University, Changchun 130025, China;
2. Shengyang Aerospace University, Mechanical and Electrical Engineering Institute, Shengyang 110136, China |
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| Abstract: | In view of the non-stationary characteristics of acoustic emission signal of tool wear,and the slow convergence rate of learning algorithm and easily dropping into the local minimum value for back propagation neural networks,a novel method of tool wear state identification based on empirical mode decomposition and least squares support vector machine was proposed.Firstly,the empirical mode decomposition method was used to decompose the collected acoustic emission signals into a number of stationary intrinsi... |
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| Keywords: | tool wear condition monitoring least squares support vector machine empirical mode decomposition auto regressive model |
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