Title :
A Frequency Band Energy Analysis of Vibration Signals for Tool Condition Monitoring
Author :
Xu Chuangwen ; Liu Zhe ; Luo Wencui
Author_Institution :
Dept. of Mech. Eng., Lanzhou Polytech. Coll., Lanzhou, China
Abstract :
The cutting vibration signals in different milling wear conditions are decomposed and reconstructed by using the wavelet packet transform and feature extraction with conditions information is obtained by using proper and scientific methods to extract from signals. The effect of cutting parameters on energy parameters was taken into account while extracting energy parameters of tool wear characteristic vector, this made the extracted parameters of characteristic vector be more sensitive to tool wear and the sensitivity to cutting parameters be the minimum. The recognition method for tool wear condition was studied through artificial neural network and wavelet packet analysis, and relevant RBF network was established. The results showed that RBFN can be successfully used to recognize and analyze tool wear conditions.
Keywords :
computerised monitoring; condition monitoring; cutting; feature extraction; mechanical engineering computing; milling machines; pattern recognition; radial basis function networks; vibrations; wavelet transforms; wear; RBF network; artificial neural network; cutting vibration signal; feature extraction; frequency band energy analysis; milling wear condition; tool condition monitoring; tool wear characteristic vector; tool wear condition recognition; wavelet packet analysis; wavelet packet transform; Artificial neural networks; Condition monitoring; Data mining; Feature extraction; Frequency; Milling; Signal analysis; Wavelet analysis; Wavelet packets; Wavelet transforms;
Conference_Titel :
Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-0-7695-3583-8
DOI :
10.1109/ICMTMA.2009.80
