EARLY, DIAGNOSIS OF AIRGAP ECCENTRICITY FAULT IN THE INVERTER DRIVEN INDUCTION MOTOR DRIVES BY WAVELET TRANSFORM

Abstract

In the present time, the inverter-driven induction motors drives are being widely employed in the industries for variable speed applications. These drives are replacing D.C. motors and thyrister bridges day by day in the industries. In the past, the Fast Fourier Transform (FFT) algorithm has been successfully implemented for the diagnosis of air-gap fault in the induction motor. This algorithm used to diagnose various induction motor faults in the steady state conditions for constant load. However, the FFT algorithm unable to diagnose induction motor faults in the transient condition. Therefore, early fault detection is not possible by this method. This research paper proposed a wavelet transform based new technique for early diagnosis of air-gap fault in the induction motor. By using this technique, the airgap fault may be diagnosed in the transient condition and fault may be averted before become more catastrophic. Therefore, early diagnosis of airgap fault is possible by this method. As a result, industries may save large revenues and unexpected failure condition. In this research paper, an inverter-driven induction motor setup has been proposed and diagnosed air-gap eccentricity fault in the transient condition by time-domain and time-frequency domain(wavelet transform) techniques. The Motor Current Signature Analysis(MCSA) technique has been used for healthy and faulty conditions of the motor. The low frequency approximation signal has been used to distinguish healthy an