DocumentCode :
2248469
Title :
Intelligent gear fault detection based on relevance vector machine with variance radial basis function kernel
Author :
He, Chuangxin ; Liu, Chengliang ; Li, Yanming ; Tao, Jianfeng
Author_Institution :
Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2010
fDate :
6-9 July 2010
Firstpage :
785
Lastpage :
789
Abstract :
Detecting machine faults at an early stage is very important. In this study, an intelligent fault detection method based on relevance vector machine (RVM) is proposed for incipient fault detection of gear. First, by combining wavelet packet transform with Fisher criterion, it is able to adaptively find the optimal decomposition level and select the global optimal features from all node energies of full wavelet packet tree. Then, RVM is adopted to train the fault detection model. Improved from Gaussian radial basis function (RBF), a new kernel function denoted variance radial basis function (VRBF) is proposed and used for RVM. Experimental results validate the effectiveness of the proposed method and demonstrate that VRBF_RVM can significantly improve generalization performance over RBF_RVM.
Keywords :
condition monitoring; fault diagnosis; gears; mechanical engineering computing; radial basis function networks; support vector machines; wavelet transforms; Fisher criterion; Gaussian radial basis function; RVM; VRBF; intelligent gear fault detection; machine fault detection; optimal decomposition level; relevance vector machine; variance radial basis function kernel; wavelet packet transform; wavelet packet tree; Fault detection; Feature extraction; Gears; Kernel; Support vector machines; Testing; Wavelet packets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Intelligent Mechatronics (AIM), 2010 IEEE/ASME International Conference on
Conference_Location :
Montreal, ON
Print_ISBN :
978-1-4244-8031-9
Type :
conf
DOI :
10.1109/AIM.2010.5695821
Filename :
5695821
Link To Document :
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