Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis

Abstract

Dissolved gas analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained ANN so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating ANN with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the inputs. The proposed method derives linear equations by approximation the hidden unit activation function and splitting the input space into subregion. For each subregion there is a linear equation. The experiments on real data indicate that the approach used can extract simple and useful rules. Transformer incipient fault diagnosis can be made that matches the actual fault present and at times the predictions better than those of the IEC/IEEE method. The rule sets generated have been successfully checked for accuracy of predictions by applying them to case studies.