Application of learning theory to a single phase induction motor incipient fault detector artificial neural network

Abstract

The generalization ability of a neural network in a specific application is of interest to many neural network designers. Learning theory, derived from maximum entropy, is applied to a neural network used for incipient fault detection in single-phase induction motors. The authors use learning theory to predict the proper number of training examples needed to reach a specific accuracy level (before actually training the network), so that excessive and unnecessary training examples and training time can be avoided. The results of learning theory are compared to actual training results to show the efficiency and reliability of the use of learning theory. >