Fault Diagnosis of a Hydro Turbine Generating Set Based on Support Vector Machine

被引：1
|
作者：
Yang, Chunting [1 ]
Tao, Jian [1 ]
Yu, Jing [2 ]
机构：
[1] Zhejiang Univ Sci & Technol, Sch Informat & Elect Engn, Hangzhou, Zhejiang, Peoples R China
[2] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON M5S 1A1, Canada
来源：
PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL III | 2009年
关键词：
support vector machine; multclass classification; fault diagnosis; hydro turbine generating set;
D O I：
10.1109/GCIS.2009.291
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With the application of large capacity hydro turbine generating set, it is important for the hydro turbine generating set to monitoring its vibration and diagnoses its faulty. In this paper, fault diagnosis based on support vector machine is proposed for hydro turbine generating set. The most important advantage of SVM is effective for the case of lack of training samples. Some key parameters of SVM and kernel functions are surveyed. Compared with the artificial neural network methods, SVM methods are more effective. The experiment shows that the SVM method has good classification ability and robust performances.
引用
收藏
页码：415 / +
页数：2
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