APPLICATION OF LEARNING THEORY TO AN ARTIFICIAL NEURAL NETWORK THAT DETECTS INCIPIENT FAULTS IN SINGLE-PHASE INDUCTION MOTORS

Abstract

The generalization ability of a neural network in a specific application is of interest to many neural network designers. In this paper, learning theory is applied to a neural network used for incipient fault detection in single-phase induction motors. This paper will show that learning theory can help determine the proper number of training examples needed to reach a specific performance level, so that excessive and unnecessary training examples can be avoided. Comparisons of the results of learning theory and Monte Carlo estimate are presented, showing that learning theory is a useful and reliable tool to obtain information about the training process of a given neural network.