The research on the method of feature selection in support vector Machine based Entropy

被引：0
|
作者：
Zhu, Xiaoyan [1 ]
Tian, Xi [1 ]
Zhu, Xiaoxun [2 ]
机构：
[1] North China Elect Power Univ, Dept Mech Engn, Baoding 071003, Hebei Province, Peoples R China
[2] North China Elect Power Univ, Dept Power Engn, Baoding 071003, Hebei Province, Peoples R China
来源：
PROGRESS IN POWER AND ELECTRICAL ENGINEERING, PTS 1 AND 2 | 2012年 / 354-355卷
关键词：
Fault diagnosis; Entropy; support vector Machine; feature selection;
D O I：
10.4028/www.scientific.net/AMR.354-355.1192
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The large rotating machinery functioning of the rotor is one of the most important issues. It has great significance to identify the fault early and implement intelligent fault diagnosis. However there is a big nonlinear about large rotating machinery and has less fault samples. This led great difficulties for feature selection and state recognition. Based on Entropy in feature selection, we extract each intrinsic mode's function energy as eigenvector and make them for input parameter of the support vector machine (SVM) to fault diagnosis. The experiment shows that this method can classify the fault state, and completed intelligent fault diagnosis.
引用
收藏
页码：1192 / +
页数：3
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