Method of Fault Diagnosis Based on SVDD-SVM Classifier

被引：1
作者：
Lv, Feng [1 ]
Li, Hua
Sun, Hao [3 ]
Li, Xiang [1 ,2 ]
Zhang, Zeyu [1 ]
机构：
[1] Hebei Normal Univ, Dept Elect, Shijiazhuang 050024, Peoples R China
[2] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
[3] Shijiazhuang Power Supply Co, Shijiazhuang 050010, Peoples R China
来源：
PROCEEDINGS OF THE 2015 CHINESE INTELLIGENT SYSTEMS CONFERENCE, VOL 1 | 2016年 / 359卷
关键词：
Support vector data description; Support vector machine; Fault diagnosis;
D O I：
10.1007/978-3-662-48386-2_7
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming at the problem of incomplete fault data samples, a fault diagnosis method based on Support vector data description and Support vector machine (SVDD-SVM) is presented. First, the data description model is build based on the normal data samples and known fault data samples, and SVM model is built based on known fault data samples. Then the test data samples are tackled by the data description model to reject or accept. The specific categories of accepted samples are diagnosed by the SVM model and the rejected samples are unknown fault types. Tests show that this method can efficiently solve the fault diagnosis problem of incomplete fault samples.
引用
收藏
页码：63 / 68
页数：6
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