Single point bearing fault diagnosis using simplified frequency model

Abstract

The time synchronous averaging (TSA) is a well-known technique used for early detection of bearing failure in electrical machines. This method is very efficient if the characteristic default frequency is perfectly known. In this article, a reduced frequency model, derived from ESPRIT algorithm, is used to provide a very accurate estimation of the fault frequency. The precision obtained on this frequency allows to apply TSA algorithm under optimal conditions. The proposed method is tested on simulated and real vibration signals for inner and outer ring faults. Finally, a fault indicator is proposed to discriminate the healthy case from the faulty one.