Use of Deep Learning Algorithms To Prevent Pantograph-Catenary Malfunctions

Year 2022, Volume: 14 Issue: 2, 394 - 405, 31.07.2022

Şakir Parlakyıldız Muhsin Gençoğlu Mehmet Sait Cengız

https://doi.org/10.29137/umagd.957018

Abstract

Today, the demand for rail transport is increasing. Studies in this area are increasing worldwide. While the railway infrastructure is increasing in the world, the suitability of the railroads and train sets built is of great importance in terms of road and passenger safety. The most important test to ensure road and passenger safety is on the electrification line. The energy required for the movement of the electric train is provided by the power line. Continuous contact between the power line and the pantograph is desired while in motion by providing continuous energy for the rail system to operate. Even short-term non-contact between the pantograph and the catenary adversely affects the rail system vehicle and the electronic systems inside. For this reason, the pantograph and catenary interaction should be controlled dynamically and statically in certain periods. In this study, dynamic and static control was provided by using deep learning. The data received from the system are recorded in CSV format. Using deep learning algorithms, failure points have been successfully detected up to 99.4%.

Keywords

Catenary, data acquisition, high speed train, fault detection, pantograph, Catenary, data acquisition, high speed train, fault detection, pantograph

Supporting Institution

TUBITAK

Project Number

EEEAG‑118E322

Thanks

This work has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant EEEAG‑118E322. (EEE‑AG: Electrical Electronics Engineering Research Group).

Pantograf-Katener Arızalarını Önlemek İçin Derin Öğrenme Algoritmalarının Kullanımı

Abstract

Günümüzde demiryolu taşımacılığına olan talep artmaktadır. Bu alanda yapılan çalışmalar dünya çapında artmaktadır. Dünyada demiryolu altyapısı artarken, inşa edilen demiryollarının ve tren setlerinin uygunluğu yol ve yolcu güvenliği açısından büyük önem taşımaktadır. Yol ve yolcu güvenliğini sağlamak için en önemli test elektrifikasyon hattındadır. Elektrikli trenin hareketi için gerekli olan enerji, elektrik hattı tarafından sağlanmaktadır. Raylı sistemin çalışması için sürekli enerji sağlanarak hareket halindeyken enerji hattı ile pantograf arasında sürekli temas istenmektedir. Pantograf ile katener arasındaki kısa süreli temassızlık bile raylı sistem aracı ve içindeki elektronik sistemleri olumsuz etkiler. Bu nedenle pantograf ve katener etkileşimi belirli periyotlarda dinamik ve statik olarak kontrol edilmelidir. Bu çalışmada derin öğrenme kullanılarak dinamik ve statik kontrol sağlanmıştır. Sistemden alınan veriler CSV formatında kaydedilmiştir. Derin öğrenme algoritmaları kullanılarak hata noktaları %99,4'e kadar başarıyla tespit edilmiştir.

Keywords

Katener, veri toplama, yüksek hızlı tren, arıza tespiti, pantograf, Katener, veri toplama, yüksek hızlı tren, arıza tespiti, pantograf

Project Number

EEEAG‑118E322

References

• Arı, D., Çıbuk M., & Ağgün, F. (2018). A New Proxy-Based Network Joining Method for Linear Wireless Sensor Networks. International Engineering and Natural Sciences Conference (IENSC 2018), 2018,715-723.
• Bucca, G., Collina, A., (2009). A Procedure for the Wear Prediction of Collector Strip and Contact Wire in Pantograph–Catenary System, Wear, 266, 46–59.
• Boguslavskii, A.A., & Sokolov. S.M., (2006). Detecting Objects in Images in Real-Time Computer Vision Systems Using Structured Geometric Models, Pleiades Publishing, Inc., 32, 177–18.
• Burrow, M., Ghataora, G., & Stirling, A. (2004). Rail Research UK, Department of Civil Engineering, University Of Birmingham, A Rational Approach To Railway Track Substructure Design, Birmingham, UK.
• Carbonell, J.G. (1983). Learning by analogy, Formulating and generalizing plans from past experience, In Machine learning, 137-161. Springer, Berlin, Heidelberg.
• Cengiz, M.S. (2013). Smart meter and cost experiment, Przeglad Elektrotechniczny 89(11), 206-209.
• Cengiz, Ç., Yapıcı, I., & Cengiz, M.S. (2018). Fourier Analysis in Rail Systems. International Conference on Multidisciplinary, Science, Engineering., & Technology (2018 Dubai, UAE).
• Cengiz, M.S., & Cengiz, Ç. (2018). Numerical analysis of tunnel lighting maintenance factor. International Islamic University Malaysia Journal, 19(2), 154-163.
• Cengiz, Ç., Eren, M., Kaynaklı, M., Yapıcı, I., Gencer, G., & Yurci, Y. (2017). Numerical Analysis of Maintenance Factor for Tunnel and Road In Solid State Lighting, International Conference on Multidisciplinary, Science, Engineering and Technology, October 27-29, 2017. Bitlis.
• Cengiz, Ç., Kaynaklı, M., Gencer, G., Eren, M., Yapici, İ., Yildirim, S., & Cengiz, MS. (2017). Selection Criteria and Economic Analysis, International Conference on Multidisciplinary, Science, Engineering, and Technology Bitlis Book of Abstracts,1 (1), 27-29.
• Cengiz, M.S. (2020). Effects of Luminaire Angle and Illumination Topology on Illumination Parameters in Road Lighting. Light & Engineering, 28(4), 47–56.
• Cengiz, MS. (2014). Evaluation of Smart Grids and Turkey, Global Advanced Research Journal of Engineering, Technology and Innovation. 3(7), 149-153.
• Çıbuk, M., & Cengiz M.S. (2020). Determination of Energy Consumption According to Wireless Network Topologies in Grid-Free, Lighting Systems. Light & Engineering, 28(2), 67–76.
• Efe, S.B. (2018). UPFC Based Real-Time Optimization of Power Systems for Dynamic Voltage Regulation. Computer Modeling in Engineering & Sciences, 116(3), 391-406.
• Efe, S.B., & Cebeci, M. (2013). Power Flow Analysis by Artificial Neural Network. International Journal of Energy and Power Engineering, 2(6), 204-208.
• Erşin, H. (2015). Raylı Ulaşımda Enerji Hesabı ve Katener Dizaynı.
• Ertuğrul, Ö.F., & Tağluk, M.E. (2017). A novel machine learning method based on generalized behavioral learning theory. Neural Computing and applications, 28(12), 3921-3939.
• Ertuğrul, Ö.F., & Tağluk, M.E. (2016). A novel machine learning method based on generalized behavioral learning theory. Neural Comput Appl. 28, 39-21.
• Ertuğrul, Ö.F. (2018). Two novel versions of and atomized feed-forward artificial neural networks, stochastic pruned stochastic. Neural Processing Letters, 48(1), 481-516.
• European St., & ards-BS EN 50317,012 Railway applications. Current collection systems. Requirements for and validation of measurements of the dynamic interaction between pantograph and overhead contact line, ISBN, 9780580707803, 2012-02-29.
• Facchinetti, A., & Bruni, S. (2012). Hardware-in-the-loop Hybrid Simulation of Pantograph–Catenary Interaction, Journal of Sound and Vibration, June 2783-2797.
• Gencer, G., Eren, M., Yildirim, S., Kaynaklı, M., Palta, O., Cengiz M.S., & Cengiz, Ç. (2017). Numerical Approach to City Road Lighting St., & ards, Imeset Book of Abstracts, Int. Conf. Mult. Sci. Eng. Tech., 12-14 July 2017 Baku.
• Kiessling, F., Puschmann, R., Schmieder, A., & Schneider, E. (2009). Contact Lines for Electric Railways, Planning, Design, Implementation, Maintenance, Publicis Corporate Publishing.
• Michalski, R.S., Mozetic, I., Hong, J., & Lavrac, N. (1986). The multi-purpose incremental learning system, AQ15 its testing application to three medical domains. Proc. AAAI 1986, 10-41.
• Matvejevs (2010). Pantograph-Catenary System Modeling Using Matlab-Simulink Algorithms, Scientific Journal of Riga Technical University Computer Science. Information Technology and Management Science, 38-44.
• Midya, S., Bormann, D., Schütte, T., & Thottappillil, R. (2009). Pantograph Arcing in Electrified Railways-Mechanism and Influence of Various Parameters-Part I, With DC Traction Power Supply, IEEE Transactions on Power Delivery, 24, 1931-1939.
• O’Donnell, C., Palacin, R., & Rosinski. J. (2006). Pantograph Damage and Wear Monitoring System, The Institution of Engineering and Technology International Conference on Railway Condition Monitoring, 29-30 Nov., Birmingham , Engl. 178–181.
• Östlund, S., Gustafsson, A., Buhrkall, L., & Skoglund, M. (2008). Condition Monitoring of Pantograph Contact Strip, Railway Condition Monitoring, 4th IET International Conference, 1–6.
• Parlakyıldız, Ş., Gençoğlu, M.T., & Cengiz, M.S. (2020). Analysis of Failure Detection and Visıbility Criteria in Pantograph-Catenary Interaction. Light & Engineering, 28(6).
• Parlakyıldız, Ş., Gençoğlu, M.T., & Cengiz, M.S. (2018). Development of Rail Systems, International Conference on Multidisciplinary, Science, Engineering, and Technology.
• Parlakyıldız, Ş., Gençoğlu, M.T., & Cengiz, M.S. (2020). Electric Train Application Study for Catenary-Pantograph Interaction, European Journal of Science and Technology, (20), 506-515.
• Schöar, R. (2013). Active Control of the Pantograph-Catenary Interaction in a Finite Element Model, Master Thesis, Division of Rail Vehicles Royal Institute of Technology (KTH), Stockholm and Institute for Dynamic Systems and Control Swiss Federal Institute of Technology (ETH)., Zurich.
• Song, Y., Liu, Z., Rxnnquist, A., Navik, P., & Liu, Z. (2020). Contact Wire Irregularity Stochastics and Effect on High-speed Railway Pantograph-Catenary Interactions. IEEE Transactions on Instrumentation and Measurement.
• Taran, M.F., R-Ayerbe P., Olaru, S., & Ticlea, A. (2013). Moving Horizon Control and Estimation of a Pantograph-Catenary System, 17th International Conference System Theory Contro Computing, 527–532.
• Taşdan, S.G. (2015). Yüksek Hızlı Tren Uygulamalarında Elektrik Besleme Sistemlerinin İncelenmesi, Niğde Üniversitesi Fen Bilimleri Enstitüsü, 255 s.
• Yıldırım, S., Yapıcı, İ., Atiç, S., Eren, M., Palta, O., Cengiz, Ç., Cengiz M.S., & Yurci, Y. (2017). Numerical Analysis of productivity and Redemption Periods in LED Illimunation. Imeset Book of Abstracts, Int. Conf. Mult. Sci. Eng. Tech., 12–14 July 2017, Baku.
• Zhang, W., Zou, D., Tan, M., Zhou, N., Li, R., & Mei, G. (2018). Review of pantograph and catenary interaction, Front. Mech. Eng., 13(2), 311-322.
• Wang, Z., Guo, F., Chen, Z., Tang, A., & Ren, Z. (2013). Research on Current-carrying Wear Characteristics of Friction Pair in Pantograph Catenary System, 59th Holm Conference on Electrical Contacts, 15.
• Wu, G., Wei, W., Gao, G., Wu, J., & Zhou, Y. (2016). Evolution of the electrical contact of dynamic pantograph-catenary system, J. Mod. Transport, 24(2), 132-138.
• Xiaodong, Z., & Yu, F. (2011). Active Self-Adaptive Control of High-Speed Train Pantograph, Power Engineering Automation Conference, 3, 152 – 156.
• Xiao-Heng, Z., Xiao-rong, G., Ze-yong, W., Li, W., & Kai, Y. (2010). Study on the Edge Detection and Extraction Algorithm in the Pantograph Slipper’s Abrasion, International Conference on Computational and Information Sciences, 474 – 477.