Application of Continuous Wavelet Features and Multi-Class Sphere SVM to Chatter Prediction

Abstract:

Article Preview

A cutting chatter forecast method based on continuous wavelet feature and multi-class spherical Support Vector Machines is studied in this paper. The method based on continuous wavelet transform extracts the cutting vibration signal feature and uses multi-class spherical Support Vector Machines to discern the chatter. In order to simplify computational complexity when binary classification SVM turn to multi-class classification, the algorithm makes every kind of samples have a spherical SVM. In the feature space identified the test sample and spherical SVM centre distance as a decision-making function. Experiments show that using combine spherical SVM with continuous wavelet feature Vector has good recognition effect in the milling chatter recognition system. Chatter inoculation forecast accuracy reaches 95%, and chatter outbreak forecast accuracy reaches 97.5%.

Info:

Periodical:

Main Theme:

Edited by:

Chengyong Wang, Ning He, Ming Chen and Chuanzhen Huang

Pages:

675-680

Citation:

S. Wu et al., "Application of Continuous Wavelet Features and Multi-Class Sphere SVM to Chatter Prediction", Advanced Materials Research, Vol. 188, pp. 675-680, 2011

Online since:

March 2011

Export:

Price:

$38.00

[1] Fei. R. Y , Wang. M: China Mechanical Engineering, 2001, Vol. 12(9): pp.1075-1079.

[2] Ding Q. Q, Feng. C. G: Journal of Vibration Engineering, 2003, Vol. 16(1): pp.41-45.

[3] Kang J, et al: Proceedings of IEEE ICAL 07, Jinan China, Aug. (2007).

[4] Kang J, et al: Proceedings of IEEE ICIC 07, Qingdao China, Aug. (2007).

[5] Scholkopf B, Smola A J: Williamson, R C, et al. Neural Computation, 2000, Vol. 12(5): pp.1207-1245.

[6] Wu Shi: Journal of the Belarusian State University, 2009, (1): p.54–57.

[7] Shawe-Taylor. J, Cristianini. N: Translated and Corrected by Zhao lingling etc. The nuclear method mode analysis . Beijing: China Machine Press, (2006).