Rolling Bearing Fault Diagnosis Methods Based on Fisher Ratio and SVM

According to the widespread problem of small sample learning on rolling bearing fault diagnosis,support vector machine(SVM) is used to complete the pattern recognition of bearing fault.In order to solve the problem of poor effect of multi-classification bearing faults owing to time-domain statistical parameters,the Wavelet Packet Decomposition(WPD) technology is introduced to extract energy coefficient of each vibration signal frequency band to construct feature vector,optimize and select feature vector though Fisher ratio method,then the SVM is used for fault pattern recognition and comparative analysis of the classification results of WPD and time-domain statistical parameters.The comparative analysis results have indicated that the SVM technology is an effective classification method for fault identification of rolling bearings.When Fisher ratio method combines with the SVM,the fault classification accuracy and time efficiency is higher than that of the traditional multidimensional time-domain and WPD,the diagnosis precision can also be improved.