In this paper, an analysis of the induction motor control scheme based on the Direct Rotor Flux Oriented Control (DRFOC) for a whole speed range, including fieldweakening (FW) regions is presented. Two field-weakening algorithms have been compared and verified through simulation with a 3.0 [kW] induction motor drive.

In this paper deep neural networks are proposed to diagnose inter-turn short-circuits of induction motor stator windings operating under the Direct Field Oriented Control method. A convolutional neural network (CNN), trained with a Stochastic Gradient Descent with Momentum method is used. This kind of deep-trained neural network allows to significantly accelerate the diagnostic process compared to the traditional methods based on the Fast Fourier Transform as well as it does not require stationary operating conditions. To assess the effectiveness of the applied CNN-based detectors, the tests were carried out for variable load conditions and different values of the supply voltage frequency. Experimental results of the proposed induction motor fault detection system are presented and discussed.