Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = $94.2$%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.

In this paper it is proposed the displacement control of a bearingless induction machine (BIM) with split winding and optimized drive structure using Active Disturbance Rejection Control (ADRC). Considering that the BIM is a multivariable, nonlinear, and time-varying system with coupled variables, advanced control techniques can be useful in order to make the system operate efficiently and with good dynamic performance. The ADRC considers the total disturbance, composed of unmodeled dynamics, nonlinearities, uncertainties, and load variations, as an extended state and estimates it in real-time through a state observer. This increases the overall robustness of the control system to disturbances of different natures. The application of the ADRC technique on the radial position control of the BIM used in this work showed that a Linear version of ADRC is not able to compensate for radial load disturbances but this drawback can be solved by the use of a nonlinear observer in the ADRC structure. Besides that, both control versions of the ADRC were able to make stable the naturally unstable radial displacement of the machine’s rotor.

This paper analyzes a Digital Signal Processor (DSP) based One Cycle Control (OCC) strategy for a Power Factor Corrector (PFC) rectifier with Common-mode Voltage (CMV) immunity. It is proposed a strategy that utilizes an emulated-resistance-controller in closed-loop configuration to set the dc-link voltage to achieve unity power factor (UPF). It is shown that if the PFC can achieve UPF condition and if the phase voltage is only affected by CMV, then phase current is free from CMV, as well as a lead-lag compensator (LLC) to average phase current.

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through accurate diagnosis and classification of faults in three-phase induction motors, using artificial intelligence techniques, by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network, using the scanning method with multiple training and validation iterations, with the introduction of new data. The results of these tests showed that the network had excellent generalization in all the situations evaluated, achieving the following accuracy rates: Motor without fault = 94.2%, Unbalance fault = 95%, Bearings with fault =98% and Stator with fault = 95%.