LAUSR

Rolling element bearings are the critical mechanical components in industrial applications. These rotary elements work continuously under different operating and environmental conditions. It leads to generation of various defects over the operating surfaces of bearing components. In this study, four machine learning techniques, two ensemble techniques, i.e., rotation forest and random subspace, and two well-established techniques, i.e., support vector machine and artificial neural network, are utilized for fault severity classification. Time domain, frequency domain and wavelet-based features are extracted from the raw vibration signals of rolling element bearings for the investigations. Four feature ranking techniques are employed to rank extracted features. Comparative study is carried out among the machine learning techniques with and without the ranking of features. Present study not only investigates various machine learning techniques but also examines the performance of various feature ranking techniques for fault severity classification of rolling element bearings. Results show that ensemble techniques have superior classification efficiency and take very less computational time for the analysis in comparison to support vector machine and artificial neural network.