Ranked feature-based data-driven bearing fault diagnosis using support vector machine and artificial neural network

This article analyzes a data-driven fault diagnosis method for rolling element bearing under different operating conditions. The work is aimed to incorporate the filter-type feature selection algorithms, namely, neighborhood component analysis, minimum redundancy maximum relevance, chi-square, and ReliefF algorithms for the feature ranking of the extracted time-domain statistical features from the collected vibration data of the five different bearing defect conditions (normal, outer race defect, ball defect, inner race defect, and combined inner and ball defect). The vibration data have been recorded and an envelope analysis technique for pre-processing the signal is used to identify the fundamental defect frequencies of all the bearings. The artificial neural network and the support vector machine are used to predict the comparative performance of the feature selection algorithms. The results demonstrate that the neighborhood component analysis algorithm achieves the highest accuracies for the bearing fault detection with both the support vector machine and artificial neural network among all the feature selection methods. Therefore, the effective feature ranking before the fault classification by the machine learning algorithms can lead to an efficient and reliable bearing fault diagnosis.