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Shock and Vibration
Volume 2016 (2016), Article ID 1582738, 9 pages
Research Article

Intelligent Analysis Method of Gear Faults Based on FRWT and SVM

Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Pingleyuan No. 100, Chaoyang District, Beijing, China

Received 5 April 2016; Revised 3 August 2016; Accepted 4 August 2016

Academic Editor: Minvydas Ragulskis

Copyright © 2016 Hongfang Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An intelligent analysis method for gear faults based on fractional wavelet transform (FRWT) and support vector machine (SVM) is proposed. Based on this method, FRWT is used to eliminate noise from the gear vibration signal, and the vibration signal after noise elimination is carried thought wavelet packet decomposition and reconstruction. A sequence corresponding to the signal is constructed consisting of the module with the highest level wavelet coefficients after decomposition and feature vectors corresponding to the energy sequence which were obtained by calculation. Then, a particle optimization method is used to optimize SVM parameters, and the feature vectors as training samples are input into SVM for training while the test samples are input for fault recognition. Experimental results show that the gear fault analysis method proposed in this paper is able to effectively extract the weak fault signal. The accuracy rate for identification of the type of gear fault reached 96.7%.