Support Vector Machine Based on Nodes Refined Decision Directed Acyclic Graph and Its Application to Fault Diagnosis

2010 ◽  
Vol 36 (3) ◽  
pp. 427-432 ◽  
Author(s):  
Hui YI ◽  
Xiao-Feng SONG ◽  
Bin JIANG ◽  
Ding-Cheng WANG
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Directed Acyclic Graph ◽  
Support Vector ◽  
Acyclic Graph

Fault Diagnosis of Power Transformer Based on DDAG-SVM

2010 ◽  
Vol 121-122 ◽  
pp. 819-824
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Fault Diagnosis ◽  
Statistical Learning ◽  
Directed Acyclic Graph ◽  
Power Transformer ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Test Results ◽  
Excellent Performance ◽  
Acyclic Graph

Support vector machine (SVM) is a novel machine learning method based on statistical learning theory. SVM is powerful for the problem with small sampling, nonlinear and high dimension. A decision directed acyclic graph(DDAG) based on SVM classifier is applied to fault diagnosis of power transformer. We optimize the structure of a decision directed acyclic graph by putting SVM with higher generalization ability at the upper nodes of the decision tree. The test results show that the classifier has an excellent performance on training speed and reliability.

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