Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

2017 3rd International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Cao, Yan^1 ; Sun, Fengru^2

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China^1

Lanzhou City University, Business School, Lanzhou, China^2

关键词: BP neural networks;    Computing time;    Fault classifier;    Least squares support vector machines;    Multi faults;    Support vector;    Track circuit;    Training sample;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/108/5/052106/pdf
DOI  :  10.1088/1755-1315/108/5/052106

来源: IOP

【 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.