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Two neural network-based schemes for fault diagnosis and identification on induction motors are presented in this paper. Fault identification is performed using self-organizing maps neural networks. The first scheme uses the information of the motor phase current for feeding the network, in order to perform the diagnosis of load unbalance and shaft misalignment faults. The network is trained using data generated through the simulation of a motor-load system model, which allows including the effects of load unbalance and shaft misalignment. The second scheme is based on the motor's active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to both networks in order to validate the proposal. It is demonstrated that the proposed strategies are able to correctly identify, both unbalanced and misaligned load, as well as broken bars and low-frequency oscillating loads, thus avoiding the need for an expert to perform the task.

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In the last few years, new efficiency requirements for induction motors have been imposed. Therefore electrical machines manufacturers have had to redefine the design criteria in order to reduce losses aiming to achieve the efficiency values established by the IE2 and IE3 standard categories according to the IEC. Given that, in the near future, an increasing demand regarding efficiency (IE4 and IE5 categories) is expected, it would be appropriate to seek new alternatives to further reduce losses in induction motors.In this work, the possibility to use magnetic wedges in induction motors with semi-closed slots is studied. This strategy allows to reduce copper and core losses, therefore increasing then the motor efficiency. The study analyses low power motors and considers different permeabilities and geometries for the magnetic wedges. In addition, it focuses attention on the starting torque and currents.Finally, an experimental validation using a 3-kW 380-V 4-pole IE2-class induction motor is presented.

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