Author/Authors :
Chen,Hongfang Beijing Engineering Research Center of Precision Measurement Technology and Instruments - Beijing University of Technology, PiChina , Sun, Yanqiang Beijing Engineering Research Center of Precision Measurement Technology and Instruments - Beijing University of Technology, PiChina , Shi, Zhaoyao Beijing Engineering Research Center of Precision Measurement Technology and Instruments - Beijing University of Technology, PiChina , Lin, Jiachun Beijing Engineering Research Center of Precision Measurement Technology and Instruments - Beijing University of Technology, PiChina
Abstract :
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%.
Keywords :
Intelligent Analysis Method , SVM , FRWT , Gear Faults
