Fault Diagnosis of Ball Bearings Using Soft Computing

This paper presents the condition monitoring and fault diagnosis of rolling element bearings using Support Vector Machines (SVM). The vibration response of healthy bearings and bearings with various component defects such as outer race, inner race, balls and their combination have been analyzed. From the obtained vibration spectrum, it is clearly seen that a discrete peak of excitation appeared for the specific defect of bearings. In this paper, various faults of the bearings has been simulated and classified. The process includes, data acquisition, feature extraction from time response and a knowledge based system to classify faults. Features defining feature vectors are formed using statistical techniques and are fed as input to the support vector machine (SVM) classifiers. Knowledge based system developed for classification can be used for automatic recognition of machinery faults based on feature vector.