Recognition of Tool Cutting State Under Least Squares Support Vector Machine
-
摘要: 基于小波包优良的时频特性和最小二乘支持向量机(least squares support vector machine,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.Abstract: A method of cutting tool condition based on wavelet packet excellent time-frequency characteristics and least squares support vector machine (LS-SVM) high-quality learning and generalization ability with small samples is presented for a cutting tool state recognition system.Minimum entropy criterion was adopted to decompose best wavelet packet for extracting feature of acoustic emission signals,the feature vectors were constructed by the AE signals energy relative percentage of each band accounted for the total energy,which were brought in multi-class LS-SVM classifier,and the classification recognition of different cutting tool states was achieved.Results show that the multi-class LS-SVM classifier is an efficient method for accurately recognizing the cutting tool states of the test samples that contain feature vectors,when γ=10(penalty factor) and σ2=1(RBF kernel parameter) in the LS-SVM multi-classification algorithm with Gaussian kernel function.
-
-
[1] JANTUNEN E.A summary of methods applied to toolcondition monitoring in drilling[J].International Journalof Machine Tools&Manufacture,2002,42:997-1010.
[2] 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.
[3] 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) [4] 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) [5] 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. [6] 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.
[7] LI Xiao-li.A brief review:acoustic emission method fortool wear monitoring during turning[J].InternationalJournal of Machine Tools&Manufacture,2002,42:157-165.
[8] 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) [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.
计量
- 文章访问数: 10
- HTML全文浏览量: 1
- PDF下载量: 7
下载:
