Comparison of Support Vector Machine and Radial Basis Artificial Neural Network in Fault Diagnosis Of Bearings Using Short Time Fourier and Choi-Williams Transforms

This paper focus on the fault diagnosis of bearing with two soft computing methods. Firstly, the vibration signal of a test rig is done. An intelligent method is selected to diagnose a ball bearing type 1206, using the vibration signal. This paper presents two methods time-frequency based approach for classifying the vibration signals of rolling bearing. It uses the features extracted from the time-frequency distribution (TFD) of the vibration signal segments. The results of applying the method to a database of real signals and then classifying feature by radial basis artificial neural network and support vector machine reveal that, for the given classification task, the selected features consistently exhibit a degree of discrimination between the vibration signals collected from healthy and fault machine. A comparison between the performances of the features extracted from two TFDs shows that the Choi-williams is better than STFT. Also the results show that the accuracy of SVM for classification is better than RBF artificial neural network.

Comparison of Support Vector Machine and Radial Basis Artificial Neural Network in Fault Diagnosis Of Bearings Using Short Time Fourier and Choi-Williams Transforms Keywords:

bearing faults , support vector machine , radial basis network