Fault Diagnosis and Prediction of Rolling Bearing Based on the Grey Support Vector Machine Model

The paper put forward a method based on GM( 1,1)-SVM for rolling bearing fault prediction and diagnosis. Firstly,time and frequency domain feature values of vibration signal of rolling bearing under all kinds of fault and normal condition were extracted. Then the important characteristic parameters were collected to build the predict model. Lastly,fault and normal condition eigenvalue was used to train binary tree support vector machine and to construct the decision tree to classify the fault type. Thus the bearing fault diagnosis and the fault prediction through the predicted values and the support vector machine( SVM) were achieved.