摘要：After briefly analyzing past research, by wavelet packet transform with support vector machine (SVM), a new method of bearing fault diagnosis is presented. Wavelet packets have greater decor relation properties than standard wavelets in that they induce a finer partitioning of the frequency domain of the process generating the data. we analyze the vibration features of testing signals of a bearing system in different running conditions by wavelet de-noising with thresholds; we decompose the feature signals into different frequency bands with the wavelet packet transform (WPT) and then calculate the energy percentage of every frequency band component to obtain its fault detection index used for fault diagnosis by the support vector machine (SVM). We analyze the vibration features of testing signals of a bearing system in different running conditions by wavelet de-noising with thresholds; and to decompose the feature signals into different frequency bands with the wavelet packet transform (WPT), through wavelet packet transform to obtain wavelet coefficients and then Energy eigenvector of frequency domain are extracted by using Shannon entropy principle. Subsequently, the extracted Energy eigenvector of frequency domain are applied as inputs to support vector machine（SVM）for bearing from internal fault. Fault state of bearing is identified by using radial basis function genetic-support vector machine. What is worth mentioning in particular is that our method can also effectively diagnose compound faults.
