For the problem of non-stationary signals of rolling bearing and the difficulty to get a large number of typical fault samples in practice

a fault diagnosis method was proposed based on dual-tree complex wavelet packet transform and support vector machine (SVM). Firstly

the non-stationary fault vibration signal was decomposed into several different frequency band components through dual-tree complex wavelet packet transform; secondly

normalization processing was made from the energy of each component. Finally

the energy characteristics parameters of each frequency band component were taken as input of the SVM to identify the fault type of rolling bearing. The analog signals of experiments

containing normal condition of the rolling bearing

crack fault of bearing outer ring

crack fault of bearing inner ring and pitting fault of bearing rolling element

were analyzed and the fault recognition rate reaches 99.5%. The proposed method can identify the working state and fault type of rolling bearing accurately and effectively

as compared with the method of combining traditional wavelet packet transform with SVM.