Comparison of the supervised machine learning techniques using WPT for the fault diagnosis of cylindrical roller bearing

Abstract

In this paper, a comparative study of the Artificial Intelligence (AI) techniques for the condition monitoring of the cylindrical roller bearing is presented. For the feature selection, wavelet analysis is applied using the ‘sym2’ as the mother wavelet. Nine features are considered for the training and evaluation of the AI techniques and then effectiveness is compared. Bearing sample data consists of four different conditions as having defective inner race, defective outer race, having defects on roller and a healthy bearing. For the preparation of the sample bearing, laser machine is used for introduction of the micro size defects on the surfaces. Support Vector Machine (SVM), Artificial neural network (ANN), and logistic regression are used with feature ranking method for the data training purpose and their effectiveness of identifying the condition is the major purpose. Feature ranking method is the new way of filtering the right data in right sequence for the data training. In results, Logistic regression found more accurate in comparison with the ANN and SVM for the cylindrical bearing.