Hybrid PCA-SVM Method for Pattern Recognition of Chatter Gestation

Author

Shao Qiang ; Feng Chang-jian ; Li Wenlong

Author_Institution

Univ. of Dalian Nat., Dalian, China

fYear

2011

fDate

5-7 Aug. 2011

Firstpage

598

Lastpage

601

Abstract

To distinguish chatter gestation, chatter recognition method based on hybrid PCA (Principal Component Analysis) and SVM (Support Vector Machine) is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and introduced to PCA-SVM for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

Keywords

cutting; fast Fourier transforms; feature extraction; mechanical engineering computing; pattern classification; principal component analysis; signal classification; support vector machines; vibrations; FFT feature extraction; FFT vector; chatter gestation recognition; chatter prediction experiment; classification; cutting process; dynamic pattern; hybrid PCA-SVM method; machine learning; pattern recognition; principal component analysis; support vector machine; vibration signal; Face; Feature extraction; Kernel; Principal component analysis; Support vector machines; Training; Vibrations; Chatter Gestation; PCA; Pattern Recognition; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on

Conference_Location

Zhangjiajie, Hunan

Print_ISBN

978-1-4577-0755-1

Electronic_ISBN

978-0-7695-4455-7

Type

conf

DOI

10.1109/ICDMA.2011.149

Filename

6051918