Bearing Fault Classification Using Statistical Features And Machine Learning Approach

Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.