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In this paper, a new approach for detection of inter-turn short in the stator winding of an induction motor is presented. Discrete Wavelet Transform using DB4 is performed on the envelopes of the windowed steady-state current signatures. The envelopes are determined by applying Hilbert Transform. Low frequency oscillations below 50 Hz were extracted from the reconstructed details at higher wavelet levels. As the envelope works on narrow band frequencies or monocomponent signal, it was analyzed using higher wavelet levels which belong to the narrow band harmonics. The RMS and Mean values of the reconstructed details and Power DetailEnergy defined as PDE were used as fault parameters to detect the faulty motor from the healthy one. It has been observed that the faulty motor produces higher fault parameters than the healthy one. Laboratory test results confirm the validity of the proposed method.

This paper developed a new technique for identification of mass-unbalance in rotor of an induction motor through envelope analysis using motor starting current at no-load The unbalanced magnetic pull due to centrifugal force developed produces excessive vibration in the rotor as well in the stator. This magnetic pull is very high at the starting which moves the rotor in the whole air gap, resulting changes in the air gap flux distribution in the stator and in the rotor. This induces voltage and generation of new signature pattern of motor current. The present method overcomes the difficulty of FFT analysis at steady state due to spectral leakage as the starting current is very high even at no load. Though DWT analysis is producing good results for transient motor starting current analysis, but selection of mother wavelet is not an easy task, if not proper, may introduce serious error. In the present improved method, the wavelet selection is not an important criteria. Envelope is the argument of the complex analytic signal which is obtained by using original motor current as the real and its Hilbert transform as the imaginary part. Since Envelope analysis works on narrow band instantaneous low frequencies, for which DWT was performed to extract low frequencies below 50 Hz. using higher order wavelet at higher level. Simultaneously this method has higher detectability and higher resolution and it can also deal with small data efficiently, so it can be used online as well as offline.. This method has been tested in a laboratory prototype.