Diagnosis of bearing faults of induction machines by vibration or current signals: a critical comparison

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 investigatedfield or research.Recently many research activities were focused on the diagnosis of bearing faults by currentsignal. Stator current components are generated at predictablefrequencies related to the electrical supply and mechanical frequenciesof bearing faults. However their detection is not always reliable, since the amplitude of fault signaturesin the current signal is very low.This paper compares the bearing fault detection capabilityobtained with vibration and current signals.To this aim a testbed is realized that allows to testvibration and current signal on a machine with healthyor faulty bearings. Signal processing techniquesfor both cases are reviewed and compared in order toshow which procedure is best suited to the differenttype of bearing faults. The paper contribution is the use of a simple and effectivesignal processing technique for both current and vibrationsignals, and a theoretical analysis of the physical link betweenfaults and current components including torque ripple effects. As expected becauseof the different nature of vibration and current, bearingfault diagnosis is effective only for those fault whose mechanicalfrequency rate is quite low.Experiments are reported that confirm the proposed approach.