Classification of Bearing Fault Size by Using Support Vector Machines

Abstract

Bearings are generally used as rolling elements in rotation machines. Faults in the rolling elements causes breakdown, and this may lead downtime and huge damages in rotating machines. On the other hand, bearings are often employed under high load and high running speed conditions. In this study, artificial faults are created on bearing inner rings by a laser beam in certain size namely 0.15 cm, 0.5 cm, 0.9 cm diameter. Vibration signals are collected by a data acquisition device in a shaft-bearing test setup. Before classifying the data, feature extraction is performed to characterize the signal. Statistical features are calculated and they are used as input to classification method. SVM classification model is employed to diagnose the size of the faults. The SVM model developed in this study classify the size of bearings faults with no prediction error. In addition, 0.1 mm error band is determined to eliminate minor bugs.