Diagnosis of Bearing Faults of Induction Machines by Vibration or Current Signals: A Critical Comparison

Abstract

Early diagnosis of faults in induction machines is an extensively investigated field, for cost and maintenance savings. Mechanical imbalances and bearing faults account for a large majority of faults in a machine, especially for small-medium size machines. Therefore their diagnosis is an intensively investigated field or research. Recently many research activities were focused on the diagnosis of bearing faults by current signal. Stator current components are generated at predictable frequencies related to the electrical supply and mechanical frequencies of bearing faults. However their detection is not always reliable, since the amplitude of fault signatures in the current signal is very low. This paper compares the bearing fault detection capability obtained with vibration and current signals. To this aim a testbed is realized that allows to test vibration and current signal on a machine with healthy or faulty bearings. Signal processing techniques for both cases are reviewed and compared in order to show which procedure is best suited to the different type of bearing faults. The paper contribution is the use of a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults and current components including torque ripple effects. As expected because of the different nature of vibration and current, bearing fault diagnosis is effective only for those fault whose mechanical frequency rate is quite low. Experiments are reported that confirm the proposed approach.