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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 3192967, 8 pages
https://doi.org/10.1155/2017/3192967
Research Article

Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization

1Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
3Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA

Correspondence should be addressed to Rongjie Yu; nc.ude.ijgnot@eijgnoruy

Received 30 May 2017; Revised 27 August 2017; Accepted 20 September 2017; Published 23 October 2017

Academic Editor: N. N. Sze

Copyright © 2017 Shize Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Turnout is one key fundamental infrastructure in the railway signal system, which has great influence on the safety of railway systems. Currently, turnout fault diagnoses are conducted manually in China; engineers are obliged to observe the signals and make problem solving decisions. Thus, the accuracies of fault diagnoses totally depend on the engineers’ experience although massive data are produced in real time by the turnout microcomputer-based monitoring systems. This paper aims to develop an intelligent diagnosis method for railway turnout through Dynamic Time Warping (DTW). We firstly extract the features of normal turnout operation current curve and normalize the collected turnout current curves. Then, five typical fault reference curves are ascertained through the microcomputer-based monitoring system, and DTW is used to identify the turnout current curve fault through test data. The analysis results based on the similarity data indicate that the analyzed five turnout fault types can be diagnosed automatically with 100% accuracy. Finally, the benefits of the proposed method and future research directions were discussed.