Iot and Artificial Intelligence for Fault Classification in Highefficiency Motors

High-efficiency three-phase induction motors are used in most industrial production processes; however, its malfunctioning may cause unexpected interruptions, putting at risk both manufacturing operations and operators. Consequently, it is desired to diagnose in real-time the most common incipient failures that may occur in this type of rotating machinery. Thus, this document presents a study of intelligent classification of incipient failures in an induction motor, diagnosis that is visualized from a dashboard in the cloud through a one-way IoT architecture. Using the traditional Park transform technique, torque (iq) and magnetizing (id) currents were obtained and analysed through the standard deviation statistical tool, to identify the dispersion of their operating amplitudes when the motor is at normal (H) or faulty (ECF and SC) operation conditions; these values were normalized and provided as input data to a classification deep neural network. The results given by this AI techni que in the diagnosis, for both the iq and id components, showed a mean accuracy of 100% for SC and a mean classification error of 20% and 25% for H and ECF, respectively.