Diagnosis of airgap eccentricity fault in the inverter driven induction motor drives by transformative techniques

Abstract

In the present paper, the airgap eccentricity fault of the induction motor has been diagnosed by digital signal processing transformative techniques in the inverter driven induction motor drives. The airgap eccentricity fault has been diagnosed in the transient condition by time domain as well as time-frequency domain techniques with the help of a proposed dynamic simulation model. In the past, many signal processing techniques had been used for various induction motor fault detection purpose such as fast Fourier transform, Hilbert transform, short term Fourier transform, etc. But, all techniques faced some sort of disadvantages. Therefore, in this paper, all shortcomings of the previous used signal processing techniques have been solved by newly wavelet transform's approximation signal. The low frequency approximation signal has been used to diagnose the eccentricity fault in the transient condition. Therefore, early fault diagnosis of the motor is possible and averted the motor before reaching in the ruinous conditions. As a result, the industries may save large revenues and unexpected failure conditions. The obtained results clearly demonstrate that the developed diagnostic technique may reliably separate airgap eccentricity fault in many stages.