NDLI: Fault Diagnosis in Railway Track Circuits Using Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Shangpeng Sun Huibing Zhao
Copyright Year	2013
Description	Author affiliation: Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, China (Huibing Zhao) \|\| State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China (Shangpeng Sun)
Abstract	This paper presents a fault diagnosis system for a key component called electrical separation joint in railway track circuits using multi-class support vector machines by one-versus-one strategy. Firstly, a track circuit electrical model is developed based on transmission line theory. A signal known as short-circuit current signal is obtained and the influences on it are then investigated for the existence of defective electrical separation joints. The signal is composed of arched curve segments, and each of the segments can be approximated by a quadratic polynomial. The coefficients of the polynomials for the first three arched segments are used as fault features to train the proposed diagnosis mode. Training parameters are selected using cross-validation technique. Polynomial and Gaussian RBF kernel functions are employed. Experiments with simulated data show that the correct diagnosis rates over 96% can be achieved using this approach, which can meet the requirement of practical application.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	345
Ending Page	350
File Size	340283
Page Count	6
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.146
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Fault diagnosis track circuit fault diagnosis support vector machines Capacitors Rail transportation Circuit faults machine learning Kernel
Content Type	Text
Resource Type	Article

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