Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

被引：1
|
作者：
Cao, Yan [1 ]
Sun, Fengru [2 ]
机构：
[1] Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou, Gansu, Peoples R China
[2] Lanzhou City Univ, Sch Business, Lanzhou, Gansu, Peoples R China
来源：
2017 3RD INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION (ESMA2017), VOLS 1-4 | 2018年 / 108卷
关键词：
D O I：
10.1088/1755-1315/108/5/052106
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.
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页数：8
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