Optimization of Sensor Orientation in Railway Wheel Detector, Using Kriging Method

DOI: 10.4236/jemaa.2011.312080   PDF   HTML     5,431 Downloads   8,737 Views   Citations


Considering the importance of axle counter function in detecting the train wheels and determining the clearance or occupancy of a track section, it is important to ensure a safe and reliable performance of this system. In this paper, in order to improve the sensor performance, the authors have focused on the orientation of magnetic sensors’ coils. In order to improve the detection capability of the system, through measuring the induced voltage in the receiver coil, it is important to adjust the relative orientation of the transmitter and receiver coils. Due to the existence of infinite relative orientations, in order to determine the optimum orientation for the sensor coils, Kriging methods which is one of the Response Surface Methodologies (RSMs) is applied. Finite Element Method (FEM) is utilized to provide sample data, as inputs to the Kriging algorithm. The analysis not only provides the optimum relative orientation of the sensor coils, it also improves analysis time, comparing to field based measurements. The analysis results are validated by the laboratory based data implemented in the control and signaling laboratory of the school of railway engineering and also field based tests in Iranian railway.

Share and Cite:

A. Zamani and A. Mirabadi, "Optimization of Sensor Orientation in Railway Wheel Detector, Using Kriging Method," Journal of Electromagnetic Analysis and Applications, Vol. 3 No. 12, 2011, pp. 529-536. doi: 10.4236/jemaa.2011.312080.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. P. C. Kleijnen, “Design and Analysis of Computational Experiments: Overview,” In: T. Bartz-Beielstein, Ed., Experimental Methods for the Analysis of Optimization Algorithm, Springer, Germany, 2010, pp. 51-72. doi:10.1007/978-3-642-02538-9_3
[2] J. P. C. Kleijnen, “Kriging Metamodeling in Simulation: A Review,” European Journal of Operation Research, Vol. 192, No. 3, 2009, pp. 707-716. doi:10.1016/j.ejor.2007.10.013
[3] R. H. Meyers and D. C. Montgomery, “Response Surface Methodology: Process and Product Optimization Using Designed Experiments,” John Wiley & Sons Ltd., 1995.
[4] G. J. Van Alphen, “Electromagnetic Compatibility between Rolling Stock and Rail-Infrastructure Encouraging European Interoperability,” Proposal to EU Specific Targeted Research Project RAILCOM, 2004.
[5] R. Bloomfield, “Fundamentals of European Rail Traffic Management System-ERTMS,” Proceedings of the 11th IET Professional Development Course on Railway Signaling and Control Systems, York, 5-9 June 2006, pp. 165-184.
[6] R. J. Hill, “Electric Railway Traction: Part 7 Electromagnetic Interference in Traction Systems,” IEE Power Engineering Journal, Vol. 11, No. 6, 1997, pp. 259-266. doi:10.1049/pe:19970610
[7] A. Zamani and A. Mirabadi, “Analysis of Sensor Orientation in Railway Axle Counters Using Response Surface Methodology,” 5th SASTech, Khavaran Higher-Education Institute, Mashhad, 2011.
[8] G. Lei, K. R. Shao, Y. Guo, J. Zhu and J. D. Lavers, “Sequential Optimization Method for the Design of Electromagnetic Device,” IEEE Transactions on Magnetic, Vol. 44, No. 11, 2008, pp. 3217-3220. doi:10.1109/TMAG.2008.2002779
[9] S. N. Lophaven, H. B. Nielsen and J. Sondergaard, “DACE: A MATLAB Kriging Toolbox Version 2.0,” Technical University of Denmark, Denmark, 2002.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.