Early Fault Diagnosis of Bearing Faults Using Vibration Signals

Abstract

As essential parts of various types of rotor machinery, rolling element bearings are vital in connecting rotor and support. When rotating, bearing failure is the most common reason of mechanical failure. To maintain the normal running of mechanical components, the detection of bearing failure is extremely important. In this study, a technology for early fault diagnosis of rolling element bearings based on vibration signals was proposed, and the following states of bearing, including normal rolling element bearing, faulty outer ring bearing, faulty inner ring bearing and rolling element failure bearing, were obtained. MSAF-20- MAX (Method of Selection of Amplitudes of Frequency-20-Maximum) was discussed as a feature extraction method, which created feature vectors classified by KNN (K-nearest Neighbour classifier), K- MEANS and SVM (Support Vector Machine).