Multiple faults detection and identification of three phase induction motor using advanced signal processing techniques

In this paper, we have presented the multiple fault detection and identification system
for three-phase induction motor. Fast Fourier Transform (FFT) is the most used signal
processing technique that offers good frequency information but failing in providing time
information and handling multiple faults identification with their occurrence time. FFT also
fails to detect non-stationary condition of the signal and unable to convey sudden changes,
start and end of the events, drifts and trends. To obtain simultaneous time frequency
information and to deal with non-stationary signals Short Time Fourier Transform (STFT)
is considered optimal technique that can clearly provide time and frequency information
both. In this research work, the multiple fault detection and identification system is presented
by employing Short Time Fourier Transform (STFT) signal processing technique. The
proposed model is designed using current signature analysis method (CSAM) for three major
faults including three phase supply imbalance, single phasing condition and breakage of
rotor bars. The system is simulated in MATLAB/SIMULINK and simulation is performed
based on healthy and unhealthy conditions of the motor. Comparative analysis between
FFT and STFT, shows STFT as a promising approach.