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Mathematical Problems in Engineering
Volume 2015, Article ID 203083, 9 pages
Research Article

A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

1School of Electric & Automation Engineering, Nanjing Normal University, Nanjing, Jiangsu 210042, China
2Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China

Received 8 December 2014; Accepted 12 March 2015

Academic Editor: Simone Bianco

Copyright © 2015 Gang Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR) will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.