Experimental investigation in induction motors using signal processing techniques for early detection of inter-turn short circuit faults

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This paper presents a method based on advanced signal processing techniques for the detection of inte-rturn short circuit faults in an induction machine. The techniques presented can be classified into power spectral density estimation techniques and time-frequency techniques. The theoretical principles of these methods are presented and their performances are compared from the stator current analysis (MCSA). This article proposes a comparative study of these signal processing techniques. One is based on the power spectral density estimation techniques, such as Periodogram and Welch periodogram. The second method is based on the Hilbert transform (HT) to extract the envelope for the stator current. Then, this signal is processed via discrete wavelet transform (DWT) for determining the faulty components in the spectrum of the stator current envelope and identifying the eigenvalues of energy levels (HDWT). This technique is powerful for the non stationary signal processing. Moreover, the combination HT-DWT has assured its ability to evaluate the severity in detecting faults of the shorted turns and it is effective for nonstationary and stationary signals to extract useful information for the detection of ITSC fault. The techniques have usd experimentally data issued from the induction motor.