Diagnosis and Classifications of Bearing Faults Using Artificial Neural Network and Support Vector Machine

Abstract

The research paper presents a comparative study of artificial neural network (ANN) and support vector machine (SVM) using continuous wavelet transforms and energy entropy approaches for fault diagnosis and classification of rolling element bearings. An experimental test rig is used to acquire the vibration signals of healthy and faulty bearings. Four real-valued base wavelets are considered. Out of these wavelets, mother base wavelet is selected on behalf of maximum energy and minimum entropy criterions and extracts the statistical features from wavelet coefficient of raw vibration signals. These statistical features are used as input of ANN and SVM for classifying the faults of bearings. Finally, Morlet wavelet is selected on the basis of energy and entropy criterions. The test results show that SVM gives the better fault diagnosis and classification accuracy than ANN.