Several reported works have studied detection and short circuit fault diagnosis in electric machines based on different models. Regarding simplifying assumptions and model conditions of use in relation to stator fault (short circuit), the X change model is proposed because all parameters are computed online. Subsequently, the electrical parameters do not depend on relative position between the stator and the rotor. In this paper, two signal processing techniques are employed for short-circuit diagnosis. The first technique is based on spectral analysis (FFT) using stator current spectral components in healthy and short circuit states at steady state (stationary state). Whereas, the second technique is based on discrete wavelet transform (DWT) considered as an ideal tool because of its signals (non stationary state) analysis ability. Tests are conducted by numerical simulation and the obtained results have showed clearly that, the signatures can be extracted to detect and locate faults.
['Saadi Sakhara', 'Salah Saad', 'Leila Nacib']
Diagnosis and detection of short circuit in asynchronous motor using three-phase model