Ultra-sound and artificial intelligence applied to the diagnostic of insulations in the field

This work studies the feasibility of implementing a system for diagnosis in the field of electrical insulation based on ultrasonic noise and artificial neural networks. Such system, proved functional under laboratory conditions, extracts spectral information from the ultrasonic noise emitted by the corona discharges that occur in electric equipment and correlates it with degrees of pollution previously defined. To achieve this classification, artificial neural networks are employed. The results show the viability of the method in the field, but they also show that its reliability is proportional to the size and diversity of the available database.