Title :
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
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;
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
DOI :
10.1109/ICDMA.2011.149