Bearing and Rotor Faults detection and diagnosis of Induction Motors using Statistical Neural Networks

Abstract

The Artificial Intelligence (AI) is revolutionizing extensively in various industrial fields. The robustness of AI comes from utilization of information processing in solving complex real world problems. Contrary to other types of artificial intelligence, the Artificial Neural Networks (ANN) can monitor any industrial process, inspired by the functionality of the human brain. This paper is devoted to the diagnosis of induction machine by using the artificial neural network based on the stator current analysis as input features. The current work aims to compare the effectiveness of both types ANN classifiers: the Radial Basis Function Network (RBF) and Probabilistic Neural Network (PNN) in asynchronous machine faults (rotor and bearing faults) detection and severity evaluation. We've proved that RBF networks are better suited for assessing the severity of defects while the PNN gives better results when differentiating between rotor and bearing defects. The results presented in this work are confirmed experimentally.