Abstract: In order to improve the recognition accuracy of SVM classification,a fault diagnosis method was proposed based on dual-tree rational-dilation complex wavelet transform and support vector machine(SVM),according to the characteristics of rolling bearing fault vibration signal.Firstly,the fault signal is decomposed into several different frequency band components through dual-tree rational-dilation complex wavelet transform.Secondly,normalization processing is made from the energy of each component.Finally,the energy characteristics parameters of each frequency band component are taken as input of the SVM to identify the fault type of rolling bearing.The experimental results prove that the proposed method can identify the fault type accurately and effectively.

Key words: Dual-tree rational-dilation complex wavelet transform,Feature extraction,SVM,Rolling bearing,Fault identification