全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于最小二乘支持向量机对刀具切削状态的识别

Keywords: 刀具状态,声发射,小波包分析,最小二乘支持向量机,模式识别

Full-Text   Cite this paper   Add to My Lib

Abstract:

基于小波包优良的时频特性和最小二乘支持向量机(leastsquaressupportvectormachine,LS-SVM)对于小样本出色的学习泛化能力,提出了一种研究刀具切削状态的方法.采用最小熵准则对声发射信号进行最佳小波包分解,以各频段的信号能量占总能量的百分比来构造特征向量,输入LS-SVM多类分类器,实现对刀具切削状态的分类识别.实验结果表明,在采用高斯核函数的LS-SVM多分类算法中,选取惩罚因子γ=10,径向基核参数σ2=1时,该分类器能对测试样本进行准确的刀具切削状态识别.

References

[1]  LI Xiao-li.A brief review:acoustic emission method fortool wear monitoring during turning[J].InternationalJournal of Machine Tools&Manufacture,2002,42:157-165.
[2]  范玉刚,李平,宋执环.动态加权最小二乘支持向量机[J].控制与决策,2006,21(10):1129-1133.FAN Yu-gang,LI Ping,SONG Zhi-huan.Dynamicweighted least squares support vector machines[J].Control and Decision,2006,21(10):1129-1133.(inChinese)
[3]  JANTUNEN E.A summary of methods applied to toolcondition monitoring in drilling[J].International Journalof Machine Tools&Manufacture,2002,42:997-1010.
[4]  SUN J,HONG G S,RAHMAN M,et al.Effective train-ing data selection in tool condition monitoring system[J].International Journal of Machine Tools&Manufacture,2006,46:218-224.
[5]  王奉涛,马孝江,邹岩崑,等.基于小波包分解的频带局部能量特征提取方法[J].农业机械学报,2004,35(5):177-180.WANG Feng-tao,MA Xiao-jiang,ZOU Yan-kun,et al.Local power feature extraction method of frequency bandsbased on wavelet packet decomposition[J].Journal ofAgricultural Machinery,2004,35(5):177-180.(inChinese)
[6]  王旭辉,黄圣国,舒平.基于最小二乘支持向量机的航空发动机故障远程诊断[J].机械科学与技术,2007,26(5):595-599.WANG Xu-hui,HUANG Sheng-guo,SHU Ping.Remotediagnosis of aeroengine's fault using LS-SVM[J].Mechanical Science and Technology for AerospaceEngineering,2007,26(5):595-599.(in Chinese)
[7]  杨建国.小波分析及其工程应用[M].北京:机械工业出版社,2005:63-67.
[8]  SRINIVASA P,RAMAKRISHNA R P K.Acousticemission analysis for tool wear monitoring in face milling[J].International Journal of Production Research,2002,40(5):1081-1093.
[9]  陈爱军.最小二乘支持向量机及其在工业过程建模中的应用[D].杭州:浙江大学信息科学与工程学院,2006.CHEN Ai-jun.The study of least squares support vectormachine and its application in industrial process modeling[D].Hangzhou:Colledge of Information Science andEngineering,Zhejiang University,2006.(in Chinese)
[10]  POYHONEN S,NEGREA M,ARKKIO A,et al.Faultdiagnostics of an electrical machine with multiple supportvector classi-fiers[C]∥Proceedings of 2002 IEEEInternational Symposium on intelligent control,Vancouver,Canada,October 27-30,2002.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133