To diagnose the electric power apparatuses, 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 build 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 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 noises 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.

Times Cited in Web of Science: 9

Times Cited in Google Scholar: 20

Cited in Books:

WoS: 2013 IEEE INTERNATIONAL CONFERENCE OF IEEE REGION 10 (TENCON) シリーズタイトル: TENCON IEEE Region 10 Conference Proceedings 発行: 2013; IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 巻: 19 号: 1 ページ: 37-44 発行: FEB 2012; IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 巻: 18 号: 5 ページ: 1584-1590 発行: OCT 2011; IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 17 271-279 FEB 2010; INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II Lecture Notes in Engineering and Computer Science 654-660 2009

Others: Min Wu, Hong Cao, Jianneng Cao, Hai-Long Nguyen, Joa?o Ba´rtolo Gomes, and Shonali Priyadarsini Krishnaswamy, An Overview of State-of-the-Art Partial Discharge Analysis Techniques for Condition Monitoring, IEEE Electrical Insulation Magazine, November/December ― Vol. 31, No. 6, 2015;