SVM Multi-class Classification based on Binary Tree for Fault Diagnosis of Hydropower units

被引：0

作者：

Zhao, Weiguo ^{[1
]}

Wang, Liying ^{[2
]}

机构：

[1] Hebei Univ Engn, Ctr Educ Technol, Handan 056038, Peoples R China

[2] Hebei Univ Engn, Coll Water Conservancy & Hydropower, Handan 056021, Peoples R China

来源：

INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL | 2012年 / 15卷 / 11A期

关键词：

SVM; Binary tree; Fault diagnosis; Hydropower units;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

SVM (Support Vector Machine) is a kind of machine learning method based on statistical learning theory which uses structural risk minimization principle. It is difficult for the general SVM to solve complex fault diagnosis and it has many defects, in this paper, a new SVM multi-class classifier model based on binary tree using "one against others" is developed, which is applied to fault diagnosis for hydropower units. The simulation results show that the proposed method is simple and feasible, and it can obtain high classification accuracy.

引用

页码：4615 / 4620

页数：6

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