Neural network classifier for faults detection in induction motors

Intelligent Systems are able technical of incorporate knowledge and, therefore, are being employed in different areas, improving and innovating conventional methods. As an example, the presence of artificial intelligence in monitoring systems to identify faults in electric motors. The purpose of such systems is to prevent unscheduled maintenance or avoid significant losses in the production line. Therefore, this paper describes the performance of two topologies of neural networks for identification of short circuit in the stator windings and bearing failures. The input data to the neural networks are statistical parameters extracted from on power supplies induction motor. Thus, the intelligent system proposed in this paper proved to be efficient and able to be implemented in monitoring systems failures in induction motors.