Optical Electronic System for Operational Control of the Railway Rails Straightness

Abstract

The paper considers principles of operation of the optical electronic devices designed for operational high-precision control of the railway rails rolling surface straightness. It provides a description of the functional scheme proposed by the authors of the optical electronic devices of the triangulation type with structured illumination, where the rolling surface straightness is assessed based on analyzing the images of the rail cross sections registered by a linear camera. A technical solution is proposed that could significantly increase control and reliability of its results, as well as ensure image registration of the rail cross sections, where the distance between them is not exceeding 1 mm with the measurement car speed of 180 km/h. Mathematical expressions were obtained to study the optical electronic devices proposed scheme influence on the threshold sensitivity of control over the points' position along the rail rolling surface. To reduce the control results error, methods of the coordinates' subpixel refinement of the structured illuminated peaks registered coordinates were used. In the course of numerical experiments, the error root-mean-square deviation dependence in measuring the rail surface points' coordinates on the signal-to-noise ratio and the structured illumination peaks width values was studied. Requirements are formulated to components of the triangular type optical electronic devices, which could be introduced in design and development of the high-precision equipment for operational control of the railway track rolling stock straightness

Please cite this article in English as:

Kolyuchkin V.Ya., Marenov N.E., Egorov A.O. Optical electronic system for operational control of the railway rails straightness. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2023, no. 3 (144), pp. 33--48 (in Russ.). DOI: https://doi.org/10.18698/0236-3933-2023-3-33-48

References

[1] Yakovleva T.G., ed. Zheleznodorozhnyy put [Railway track]. Moscow, Transport Publ., 1999.

[2] Falamarzi A., Moridpour S., Nazem M. A review on existing sensors and devices for inspecting railway infrastructure. Jurnal Kejuruteraan, 2019, vol. 31, no. 1, pp. 1--10. DOI: https://doi.org/10.17576/jkukm-2019-31(1)-01

[3] Glazunov D.V. Diagnostic and technological methods for track reliability increase. Naukoemkie tekhnologii v mashinostroenii [Science Intensive Technologies in Mechanical Engineering], 2019, no. 1, pp. 32--40 (in Russ.).

[4] Liu S., Wang Q., Luo Y. A review of applications of visual inspection technology based on image processing in the railway industry. Transp. Saf. Environ., 2019, vol. 1, no. 3, pp. 185--204. DOI: https://doi.org/10.1093/tse/tdz007

[5] Ye J., Stewart E., Roberts C. Use of a 3D model to improve the performance of laser-based railway track inspection. Proc. Inst. Mech. Eng. F: J. Rail and Rapid Transit, 2019, vol. 233, no. 3, pp. 337--355. DOI: https://doi.org/10.1177/0954409718795714

[6] Yi B., Yang Y., Yi Q., et al. Novel method for rail wear inspection based on the sparse iterative closest point method. Meas. Sc. Technol., 2017, vol. 28, no. 12, art. 125201. DOI: https://doi.org/10.1088/1361-6501/aa8691

[7] Donges A., Noll R. Laser triangulation. In: Laser measurement technology. Berlin, Heidelberg, Springer, 2015, pp. 247--278. DOI: https://doi.org/10.1007/978-3-662-43634-9_10

[8] Pears N., Liu Y., Bunting P. 3D imaging, analysis and applications. London, Springer, 2012.

[9] Geng J. Structured-light 3D surface imaging: a tutorial. Adv. Opt. Photonics, 2011, vol. 3, no. 2, pp. 128--160. DOI: https://doi.org/10.1364/AOP.3.000128

[10] Bell T., Li B., Zhang S. Structured light techniques and applications. In: Wiley encyclopedia of electrical and electronics engineering. New York, John Wiley & Sons, 2016. DOI: https://doi.org/10.1002/047134608X.W8298

[11] Liu Z., Sun J., Wang H., et al. Simple and fast rail wear measurement method based on structured light. Opt. Lasers Eng., 2011, vol. 49, no. 11, pp. 1343--1351. DOI: https://doi.org/10.1016/j.optlaseng.2011.05.014

[12] Gazafrudi S.-M.M., Younesian D., Torabi M. A high accuracy and high speed imaging and measurement system for rail corrugation inspection. IEEE Trans. Ind. Electron., 2021, vol. 68, no. 9, pp. 8894--8903. DOI: https://doi.org/10.1109/TIE.2020.3013748

[13] Kruzhilov I.S. Optimal picture size for the purpose of minimal detection error of coordinate. Kompyuternaya optika [Computer Optics], 2009, vol. 33, no. 2, pp. 210--215 (in Russ.).

[14] Kulikov E.I., Trifonov A.P. Otsenka parametrov signalov na fone pomekh [Estimation of signal parameters against a background of interference]. Moscow, Sovetskoe radio Publ., 1978.

[15] Fisher R.B., Naidu D.K. A comparison of algorithms for subpixel peak detection. In: Image technology. Berlin, Heidelberg, Springer, 1996, pp. 385--404. DOI: https://doi.org/10.1007/978-3-642-58288-2_15

[16] Blais F. Review of 20 years of range sensor development. J. Electron. Imaging, 2004, vol. 13, no. 1, pp. 231--243. DOI: https://doi.org/10.1117/1.1631921

[17] Forest J., Salvi J., Cabruja E., et al. Laser stripe peak detector for 3D scanners. A FIR filter approach. Proc. ICPR, 2004, vol. 3, pp. 646--649. DOI: https://doi.org/10.1109/ICPR.2004.1334612