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http://dx.doi.org/10.7782/JKSR.2017.20.2.223

A Study on a Diagnosis System for HSR Turnout Systems (II)  

Kim, Youngseok (Vitzrosys)
Yoon, Yeonjoo (Vitzrosys)
Back, Inchul (Korea Railroad corp.)
Ryu, Youngtae (Korea Railroad corp.)
Han, Hyunsu (JVG co.LTD.)
Hwang, Ankyu (JVG co.LTD.)
Kang, Hyungseok (Seoul National University of Science&Technology)
Lee, Jongwoo (Seoul National University of Science&Technology)
Publication Information
Journal of the Korean Society for Railway / v.20, no.2, 2017 , pp. 223-233 More about this Journal
Abstract
The railway turnout system is one of the most important systems that set train routes. Turnout system integrity should be guaranteed for robust train operation. To diagnose the turnout system status, LVDT and accelerometers are installed on a turnout system in a high speed line. The LVDT and accelerometers produce signals containing physical meaning of the turnout systems. The LVDT produces the displacement of the rail gauge and vibration when point moving or a train passes on turnout systems and the accelerometer produces impact forces induced by wheel sets. We performed data extraction from the measured signals and parameterized the extracted signals into meaningful quantities. The parameters are used for classifying whether the turnout status is normal. We proposed two methods for the classification, one uses probabilistic distribution and the other artificial neuron networks. The probabilistic distribution is used for the parameter being classified by the quantities and the artificial neuron networks for the form classification. Finally, we show how to learn the normal status of a turnout system.
Keywords
Turnout; Diagnosis; Classification; Distribution; Neural networks;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 H. Bai, C. Roberts, C. Goodman (2008) A Generic Fault Detection and Diagnosis Approach for Railway Assets, University of Birmingham UK, http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4730853 (Accessed 16 January 2017)
2 A. Ekberg, B. Paulsson (2010) Concluding Technical Report Railway Assets UIC, UIC.
3 Point Dianostic System SIDIS W (Siemens), https://w3.usa.siemens.com/mobility/us/Documents/en/rail-solutions/rail-automation/signaling- components/sidis-w-en.pdf (Accessed 19 January 2017)
4 D.J. Pedregal, F.P. Garcia, F. Schmid (2004) RCM predictive maintenance of railway system based on unobserved component models, Reliability Engineering and System Safety, 83, pp. 103-110.   DOI
5 Rail Accident Investigation Branch (2007) Derailment at Grayrigg, Department for Transport UK, https://www.gov.uk/raib-reports/derailment-at-grayrigg (Accessed 22 January 2017)
6 K. Gurney (1997) An Introduction to neural network, UCL Press Limited 11 New Fetter Lane London EC4P 4EE, UCL Press, pp. 1-317.
7 R. Rojas (1996) Neural Networks, A Systematic Introduction, Springer, Springer-Verlag, Berlin, New-York, pp. 1-502.
8 B. Krose, P. van der Smagt (1996) An introduction to Nural Networks, University of Amsterdam, Institute of Robotics and System Dynamics German Aerospace Research Establishment, pp. 1-135.
9 M.T. Hagan, H.B. Demuth, M.H. Beale, O. de Jess (2014) Neural Network Design, Oklahoma State University, 2nd edition. http://dl.acm.org/citation.cfm?id=2721661 (Accessed 6 March 2017)
10 J.W. Lee, E.-S. Choo, M.-S. Kim, H.-Y. Yoo, C.-S. Mo, E.-S. Son, S.G. Park. (2015) A study on a monitoring system for railway turnout systems, Journal of the Korea Society for Railway, 18(5), pp. 439-446.   DOI