Ball Bearing Fault Diagnosis using Supervised and Unsupervised Machine Learning Methods

Abstract

This paper deals with the approach of using multiscale permutation entropy as a tool for feature selection for fault diagnosis in ball bearings. The coefficients obtained from the wavelet transformation of the vibration signals of the bearings are used for the calculation of statistical parameters. Based on the minimum multiscale permutation en-tropy criteria, the best scale is selected and statistical parameters such as crest factor, form factor, and permutation entropy are calculated. Finally, the faults are classified by considering the statistical parameters and permutation entropy as features in supervised and unsupervised machine learning methods, such as a support vector machine and self-organizing maps, respectively. Results revealed that the multiscale permutation entropy-based feature extraction techniques provide higher classification accuracy in comparison to the other methodologies that have been proposed in previous published works. The methodology proposed in this paper also gives good results for unsupervised learning methods, i.e. self-organizing maps.