Title :
Accurate diagnosis of rolling bearing based on wavelet packet and genetic-support vector machine
Author :
Xu, Yunjie ; Xiu, Shudong
Author_Institution :
Coll. of Eng., Zhejiang Forestry Univ., LinAn, China
Abstract :
This paper studies on the combination usage of wavelet packet and artificial genetic-support vector machine in the fault diagnosis of ball bearing. Energy eigenvector of frequency domain is extracted using wavelet packet analysis method. Fault state of ball bearing is identified by using radial basis function genetic-support vector machine. The test results show that this GSVM model is effective to detect fault of ball bearing.
Keywords :
ball bearings; fault diagnosis; mechanical engineering computing; radial basis function networks; rolling bearings; support vector machines; GSVM model; accurate diagnosis; ball bearing; energy eigenvector; fault diagnosis; frequency domain; genetic-support vector machine; radial basis function; rolling bearing; wavelet packet analysis method; Application software; Ball bearings; Diagnostic expert systems; Educational institutions; Fault diagnosis; Rolling bearings; Support vector machine classification; Support vector machines; Testing; Wavelet packets; bearing; fault diagnosis; genetic-support vector machine; wavelet packet;
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7737-1
DOI :
10.1109/MACE.2010.5535701
