Abstract
Derailment coefficient is an important criterion to evaluate the operating safety of rail vehicles. A derailment coefficient prediction method based on neural network is proposed in this paper. First, the basic concepts of derailment coefficient are briefly discussed. Then the principle of BP and NARX networks and their related learning rules are presented. BP network is compared to NARX network and their disadvantages are outlined. Finally, BP and NARX neural networks are established to analyze their prediction performances. The experimental result shows that, compared with BP neural network, NARX neural network offers better predictive performance of the derailment coefficient.
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References
Zeng J, Guan Q (2007) Wheelset climb derailment criteria for evaluation of railway vehicle running safety. J Traffic Transp Eng7(6):1–5 (in Chinese)
Zhai W, Chen G (2001) Method and criteria for evaluation of wheel derailment based on wheel vertical rise. J China Railw Soc 23(2):17–26 (in Chinese)
Guan Q (2006) Study on the derailment mechanism and running safety of trains. Southwest Jiaotong University, Beijing
Chang C, Wang C, Li L (2007) Study on numerical test of instrumented wheelset for continuous measurement. Railw Locomot Car 27(5):50–54 (in Chinese)
Christoph, W (2010) State-of-the-art railway vehicle design with multi-body simulation. J Mech Syst Transp Logist 3(1):12–26
Huo W (2007) Vehicle-track coupling dynamics, 2nd edn. China Railway Publishing House, Beijing (in Chinese)
Luber B (2009) Railway track quality assessment method based on vehicle system identification. Elektrotechnik und Informationstechnik 126(5):180–185
Fujie X, Colin C, Peter W (2007) An inverse railway wagon model and its applications.Veh Syst Dyn 45(6):583–605 (in Chinese)
Gualano L, Iwnicki SD, Ponnapalli PVS, Allen PD (2006) Prediction of wheel-rail forces, derailment and passenger comfort using artificial neural networks. In: Proceedings of the EURNEX-ZEL conference, Zilina, Slovakia 30th–31st May 2006
Wong CX, Worden K (2007) Generalised NARX shunting neural network modelling of friction. Mech Syst Signal Process 21(1):553–572
Zhang S, Kang X, Liu X (2008) Characteristic analysis of the power spectral density (PSD) of track irregularity on Beijing–Tianjin inter-city railway. China Railw Sci 29(5):25–29
Acknowledgments
This research was sponsored by National High-tech R&D Program of China (863 Program, No.2011AA110501) and National Key Technology R&D Program of China (No. 2011BAG01B05). The supports are gratefully acknowledged.
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Yu, X., Liu, G., Qin, Y., Zhang, Y., Xing, Z. (2014). The Prediction of Derailment Coefficient Based on Neural Networks. In: Jia, L., Liu, Z., Qin, Y., Zhao, M., Diao, L. (eds) Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume II. Lecture Notes in Electrical Engineering, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53751-6_27
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DOI: https://doi.org/10.1007/978-3-642-53751-6_27
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