Multi-domain Bearing Fault Diagnosis using Support Vector Machine
Rismaya Kumar Mishra, Anurag Choudhary, A.R. Mohanty, Shahab Fatima
Abstract
Faults in rolling element bearings are the main cause of rotating machine failure. Locating and isolating the faults has become a critical concern for the stable operation of rotating machinery. This paper puts forward a methodology to derive optimum fault indicators from vibration signature and to make a robust model using Support Vector Machine (SVM) for bearing fault diagnosis. The vibration signatures are collected at three speeds at a constant loading condition. Thereafter, optimal statistical features are extracted in the time, frequency, and time-frequency domain. The proposed technique involves the performance comparison of SVM models trained with optimal features. Results show that the multi-domain time-frequency features gave a better performance as compared to the individual domain signals.