[an error occurred while processing this directive]
   
 
���¿��ټ��� �߼�����
   ��ҳ  �ڿ�����  ��ί��  Ͷ��ָ��  �ڿ�����  ��������  �� �� ��  ��ϵ����
�������պ����ѧѧ�� 2011, Vol. 37 Issue (2) :144-148    DOI:
���� ����Ŀ¼ | ����Ŀ¼ | ������� | �߼����� << | >>
����EMD��LS-SVM�ĵ���ĥ��ʶ�𷽷�
��ɽ1, ����ɽ1, ����2*
1. ���ִ�ѧ ��е��ѧ�빤��ѧԺ, ���� 130025;
2. �������պ����ѧ ���繤��ѧԺ, ���� 110136
Identification method of tool wear based on empirical mode decomposition and least squares support vector machine
Guan Shan1, Wang Longshan1, Nie Peng2*
1. College of Mechanical Science and Engineering, Jilin University, Changchun 130025, China;
2. Shengyang Aerospace University, Mechanical and Electrical Engineering Institute, Shengyang 110136, China

ժҪ
�����
�������
Download: PDF (0KB)   HTML 1KB   Export: BibTeX or EndNote (RIS)      Supporting Info
ժҪ ��Ե���ĥ���������źŵķ�ƽ��������BP������ѧϰ�㷨�����ٶ�����������ֲ���Сֵ������,����˻��ھ���ģ̬�ֽ����С����֧���������ĵ���ĥ��״̬ʶ�𷽷�.���ȶ��������źŽ��о���ģ̬�ֽ�,����ֽ�Ϊ���ɸ�����ģ̬����֮��,Ȼ��ֱ��ÿһ������ģ̬���������Իع齨ģ,�����ȡÿһ���Իع�ģ�͵�ϵ�������������,������������Ϊ����,һ�����ڶ���С����֧��������ѵ��,��һ������ʶ�𵶾�ĥ��״̬.����������:�÷����ܺܺõ�ʶ�𵶾�ĥ��״̬,��BP��������Ⱦ��и��ߵ�ʶ����.
Service
�ѱ����Ƽ�������
�����ҵ����
�������ù�����
Email Alert
RSS
�����������
�ؼ����� ����ĥ��״̬ʶ��   ��С����֧��������   ����ģ̬�ֽ�   �Իع�ģ��     
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 intrinsic mode function, and then autoregressive model of each intrinsic mode function was established respectively. Finally, auto regression model coefficients were selected to constitute the feature vector. The feature was divided into two groups, one group was used to train the least squares support vector machine and the other was used to identify the tool wear state. The identification result proves that this method is superior to neural network, and it has a higher identification rate. It is proved that this method is efficient and feasible.
Keywords�� tool wear condition monitoring   least squares support vector machine   empirical mode decomposition   auto regressive model     
Received 2010-08-03;
Fund:

����ʡ�������ص�ʵ����������Ŀ(LS2010117)

About author: �� ɽ(1970-),��,����ʡ������,������,guanshan1970@yahoo.cn.
���ñ���:   
��ɽ, ����ɽ, ����.����EMD��LS-SVM�ĵ���ĥ��ʶ�𷽷�[J]  �������պ����ѧѧ��, 2011,V37(2): 144-148
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 A, 2011,V37(2): 144-148
���ӱ���:  
http://bhxb.buaa.edu.cn//CN/     ��     http://bhxb.buaa.edu.cn//CN/Y2011/V37/I2/144
Copyright 2010 by �������պ����ѧѧ��