Application of vibration signals in railway track diagnostics using a mobile railway platform

Authors

DOI:

https://doi.org/10.61089/aot2024.gk7vs246

Keywords:

railway, rail diagnostic, rail defects, monitoring system, railway infrastructure

Abstract

The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.

References

1. Arcieri, G., Hoelzl, C., Schwery, O., Straub, D., Papakonstantinou, K. G., & Chatzi, E. (2024). POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance. Machine Learning, 1-29. https://doi.org/10.1007/s10994-024-06559-2.

2. Arcieri, G., Rigoni, T., Hoelzl, C., Haener, D., Chatzi, E. (2024). Zurich, E. T. H., & SBB, S. F. R. Ground penetrating radar for moisture assessment in railway tracks: An experimental investigation. Journal of Physics Conference Series. https://doi.org/ 10.58286/29747.

3. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., & Inman, D. J. (2021). A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mechanical systems and signal processing, 147, 107077. https://doi.org/10.1016/j.ymssp.2020.107077.

4. Barbosa, B., Serrador, A., & Casaleiro, J. (2023). Condition Monitoring Maintenance in Train Bogie: A Low-Cost Acceleration Sensor Proposal. In 2023 International Conference on Control, Automation and Diagnosis (ICCAD) (1-4). IEEE. https://doi.org/10.1109/ICCAD57653.2023.10152337.

5. Carnevale, M., La Paglia, I., & Pennacchi, P. (2021). An algorithm for precise localization of measurements in rolling stock-based diagnostic systems. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 235(7), 827-839. https://doi.org/10.1177/0954409720965798.

6. Chrzan, M. (2022). Effect of uniform time on the transmission of signals in rail open systems. Archives of Transport, 61(1), 39-49. https://doi.org/10.5604/01.3001.0015.8150.

7. Chudzikiewicz, A., & Kostrzewski, M. (2013). Analiza sygnałów wibroakustycznych w procesie monitorowania stanu zawieszenia pojazdów szynowych oraz toru. Pojazdy Szynowe, 1(2013), 10-17. https://doi.org/10.53502/RAIL-139165.

8. De Rosa, A., Alfi, S., & Bruni, S. (2019). Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measurements. Mechanical Systems and Signal Processing, 116, 606-623. https://doi.org/10.1016/j.ymssp.2018.06.041.

9. Di Summa, M., Griseta, M. E., Mosca, N., Patruno, C., Nitti, M., Renò, V., & Stella, E. (2023). A review on deep learning techniques for railway infrastructure monitoring. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3309814.

10. Faccini, L., Karaki, J., Di Gialleonardo, E., Somaschini, C., Bocciolone, M., & Collina, A. (2023). A methodology for continuous monitoring of rail corrugation on subway lines based on axlebox acceleration measurements. Applied Sciences, 13(6), 3773. https://doi.org/10.3390/app13063773.

11. Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., & De Schutter, B. (2016). Deep convolutional neural networks for detection of rail surface defects. In 2016 International joint conference on neural networks (IJCNN) 2584-2589. IEEE. https://doi.org/10.1109/IJCNN.2016.7727522.

12. Firlik, B., Czechyra, B., & Chudzikiewicz, A. (2012). Condition monitoring system for light rail vehicle and track. Key Engineering Materials, 518, 66-75. https://doi.org/10.4028/www.scientific.net/KEM.518.66.

13. Hoelzl, C., Arcieri, G., Ancu, L., Banaszak, S., Kollros, A., Dertimanis, V., & Chatzi, E. (2023). Fusing expert knowledge with monitoring data for condition assessment of railway welds. Sensors, 23(5), 2672. https://doi.org/10.3390/s23052672.

14. Hoelzl, C., Dertimanis, V., Ancu, L., Kollros, A., & Chatzi, E. (2023). Vold–Kalman filter order tracking of axle box accelerations for track stiffness assessment. Mechanical Systems and Signal Processing, 204, 110817. https://doi.org/10.1016/j.ymssp.2023.110817.

15. Hoelzl, C., Dertimanis, V., Landgraf, M., Ancu, L., Zurkirchen, M., & Chatzi, E. (2022). On-board monitoring for smart assessment of railway infrastructure: A systematic review. The Rise of Smart Cities, 223-259. https://doi.org/10.1016/B978-0-12-817784-6.00015-1

16. Hoelzl, C., Keller, L., Simpson, T., Stoura, C., Kossmann, C., & Chatzi, E. (2024). Data-Driven Railway Vehicle Parameter Tuning using Markov-Chain Monte Carlo Bayesian updating. In Journal of Physics: Conference Series 2647 (18), 2024. IOP Publishing. https://doi.org/10.1088/1742-6596/2647/18/182024.

17. Kostrzewski, M., & Melnik, R. (2021). Condition monitoring of rail transport systems: A bibliometric performance analysis and systematic literature review. Sensors, 21 (14), 4710. https://doi.org/10.3390/s21144710.

18. La Paglia, I., Carnevale, M., Corradi, R., Di Gialleonardo, E., Facchinetti, A., & Lisi, S. (2023). Condition monitoring of vertical track alignment by bogie acceleration measurements on commercial high-speed vehicles. Mechanical Systems and Signal Processing, 186, 109869. https://doi.org/10.1016/j.ymssp.2022.109869.

19. La Paglia, I., Corradi, R., & Carnevale, M. (2022). Continuous monitoring of rail vehicle dynamics by means of acceleration measurements. In Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance, Civil-Comp Press, Edinburgh, UK, 300 (1). https://doi.org/10.4203/ccc.1.27.4.

20. Licow, R. (2018). Ocena uszkodzeń powierzchni tocznej szyn kolejowych za pomocą zjawisk wibroakustycznych, [Doctoral dissertation, Poznan University of Technology]. Poznan University of Technology Repository. https://sin.put.poznan.pl/dissertations/details/d106.

21. Mori, H., Tsunashima, H., Kojima, T., Matsumoto, A., & Mizuma, T. (2010). Condition monitoring of railway track using in-service vehicle. Journal of Mechanical Systems for Transportation and Logistics, 3(1), 154-165. https://doi.org/10.1299/jmtl.3.154.

22. Muñoz, S., Ros, J., Urda, P., & Escalona, J. L. (2021). Estimation of lateral track irregularity through Kalman filtering techniques. IEEE Access, 9, 60010-60025. https://doi.org/10.1109/ACCESS.2021.3073606.

23. Naganuma, Y., Kobayashi, M., & Okumura, T. (2010). Inertial measurement processing techniques for track condition monitoring on shinkansen commercial trains. Journal of Mechanical Systems for Transportation and Logistics, 3(1), 315-325. https://doi.org/10.1299/jmtl.3.315.

24. Nielsen, J. C., & Li, X. (2018). Railway track geometry degradation due to differential settlement of ballast/subgrade–Numerical prediction by an iterative procedure. Journal of Sound and Vibration, 412, 441-456. https://doi.org/10.1016/j.jsv.2017.10.005.

25. Nielsen, J. C., Berggren, E. G., Hammar, A., Jansson, F., & Bolmsvik, R. (2020). Degradation of railway track geometry–Correlation between track stiffness gradient and differential settlement. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 234(1), 108-119. https://doi.org/10.1177/0954409718819581.

26. Office of Rail Transport in Poland. (2023). Data on rail transport. https://www.utk.gov.pl (Access from 15.06.2024).

27. ONO, H., TSUNASHIMA, H., TAKATA, T., & OGATA, S. (2023). Development and operation of a system for diagnosing the condition of regional railways tracks. Mechanical Engineering Journal, 10(3), 22-00239. https://doi.org/10.1299/mej.22-00239.

28. Rodriguez, A., Sanudo, R., Miranda, M., Gomez, A., & Benavente, J. (2021). Smartphones and tablets applications in railways, ride comfort and track quality. Transition zones analysis. Measurement, 182, 109644. https://doi.org/10.1016/j.measurement.2021.109644.

29. Rosano, G., Massini, D., Bocciolini, L., Zappacosta, C., Di Gialleonardo, E., Somaschini, C., ... & Pugi, L. (2024). Diagnostics of the railway track-Possibility of development through the measurement of accelerations and contact forces| La diagnostica dell’armamento ferroviario-Possibilità di sviluppo attraverso la misura di accelerazioni e forze di contatto. INGEGNERIA FERROVIARIA, 79(2), 81-102.

30. Sadri, M., Lu, T., & Steenbergen, M. (2020). Railway track degradation: The contribution of a spatially variant support stiffness-Global variation. Journal of Sound and Vibration, 464, 114992. https://doi.org/10.1016/j.jsv.2019.114992.

31. Sadri, M., Lu, T., & Steenbergen, M. (2019). Railway track degradation: The contribution of a spatially variant support stiffness-Local variation. Journal of Sound and Vibration, 455, 203-220. https://doi.org/10.1016/j.jsv.2019.05.006.

32. Stoura, C. D., Dertimanis, V. K., Hoelzl, C., Kossmann, C., Cigada, A., & Chatzi, E. N. (2023). A Model‐Based Bayesian Inference Approach for On‐Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network. Structural Control and Health Monitoring, 2023(1), 8855542. https://doi.org/10.1155/2023/8855542.

33. Tsunashima, H., Naganuma, Y., & Kobayashi, T. (2014). Track geometry estimation from car-body vibration. Vehicle System Dynamics, 52, 207-219. https://doi.org/10.1080/00423114.2014.889836.

34. Tsunashima, H., Ono, H., Takata, T., & Ogata, S. (2023). Development and Operation of Track Condition Monitoring System Using In-Service Train. Applied Sciences, 13(6), 3835. https://doi.org/10.3390/app13063835.

35. Uhl, T., Mendrok, K., & Chudzikiewicz, A. (2010). Rail track and rail vehicle intelligent monitoring system. Archives of Transport, 22, 495-510. https://doi.org/10.2478/v10174-010-0030-1.

36. UNE EN 13848-1:2020 Railway applications - Track - Track geometry quality - Part 1: Characterization of track geometry.

37. Wang, H., Berkers, J., van den Hurk, N., & Layegh, N. F. (2021). Study of loaded versus unloaded measurements in railway track inspection. Measurement, 169, 108556. https://doi.org/10.1016/j.measurement.2020.108556.

38. Wang, C., Xiao, Q., Liang, H., Liu, Y., & Cai, X. (2006). On-line monitoring of railway deformation using acceleration measurement. In 2006 6th World Congress on Intelligent Control and Automation 2, 5828-5832. IEEE. https://doi.org/10.1109/WCICA.2006.1714194.

39. Westeon, P. F., Ling, C. S., Roberts, C., Goodman, C. J., Li, P., & Goodall, R. M. (2007). Monitoring vertical track irregularity from in-service railway vehicles. Proceedings of the institution of mechanical engineers, Part F: Journal of Rail and Rapid Transit, 221(1), 75-88. https://doi.org/10.1243/0954409JRRT65.

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Published

2024-09-30

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Original articles

How to Cite

Licow, R., & Franciszek Tomaszewski. (2024). Application of vibration signals in railway track diagnostics using a mobile railway platform. Archives of Transport, 71(3), 127-145. https://doi.org/10.61089/aot2024.gk7vs246

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