To diagnose the electric power apparatus, the decision tree method can be a highly recommended classification tool because it provides the if-then-rule in visible, and thus we may have a possibility to connect the physical phenomena to the observed signals. The most important point in constructing the diagnosing system is to make clear the relations between the faults and the corresponding signals. Such a database system can be built up in the laboratory using a model electric power apparatus, and we have made it. The next important thing is the feature extraction. We used phi - V - n patterns and POW patterns for feature variables, and feature extraction is made by the extended moments, usual moments, and the parameters in the underlying distributions such as the generalized normal distribution and the Weibull distribution. By simple arrangements, we will be able to classify the faults and noise with high accuracy such that the misclassification rate is lower than 5%. If we set appropriate pre-processing procedure carefully, we might have a possibility of classification accuracy of less than 2%. Therefore, the decision tree with adequate feature extraction is considered to be a promising method as one of the classification tools.
