Identification of Railway Vertical Track Alignment via the Unknown Input Observer
Source abstract
Abstract
In this paper, a model-based approach for the identification of the railway vertical track alignment from simulation data is presented. The proposed methodology is based on the application of the unknown input observer algorithm. The model of a conventional train is used to simulate the acceleration levels that vehicle-mounted sensors (e.g., on the bogies and carbody) would measure during operation. Simulations are carried out at a constant speed on both straight and curved tracks, including different types of track geometry components (namely longitudinal level, alignment, and cross-level) to assess the algorithm capability to identify the input irregularity. The primary focus is on the identification of mean vertical track alignment, a critical irregularity component for safety issues. In the analysed cases, the comparison between the measured and reconstructed signal histories are quite satisfactory, with maximum errors in the order of 15% and 29% along straight and curved tracks. Comparing the frequency content of the signals, a significantly higher degree of accuracy is observed (with maximum errors of 5–10% depending on the track layout), which demonstrates that the proposed methodology is suitable for track irregularity identification and monitoring purposes using an instrumented vehicle.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Railway Engineering and Dynamics · Vehicle License Plate Recognition · Civil and Geotechnical Engineering Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Funding
MOST – Sustainable Mobility National Research Centre
References (34)
Show source-provided references
1. (2017). Railway Applications—Track—Track Geometry Quality. Part 5: Geometry Quality Levels—Plain Line, Switches and Crossings (Standard No. EN 13848-5:2017).
2. Perspectives on Railway Track Geometry Condition Monitoring from In-Service Railway Vehicles10.1080/00423114.2015.1034730 ↗
3. 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
4. In-Service Condition Monitoring of Rail Tracks
5. Modern Tendencies in Vehicle-Based Condition Monitoring of the Railway Track
6. Condition Monitoring of Vertical Track Alignment by Bogie Acceleration Measurements on Commercial High-Speed Vehicles10.1016/j.ymssp.2022.109869 ↗
7. Acceleration-Based Condition Monitoring of Track Longitudinal Level Using Multiple Regression Models10.1177/09544097231201513 ↗
8. Limongelli, M.P., Giordano, P.F., Quqa, S., Gentile, C., and Cigada, A. A Methodology to Estimate Railway Track Conditions from Vehicle Accelerations Based on Multiple Regression. Proceedings of the Experimental Vibration Analysis for Civil Engineering Structures.
9. Limongelli, M.P., Giordano, P.F., Quqa, S., Gentile, C., and Cigada, A. Monitoring Railway Infrastructure Through a Freight Wagon Equipped with Smart Sensors. Proceedings of the Experimental Vibration Analysis for Civil Engineering Structures.
10. La Paglia, I., Araya Reyes, C.E., Di Gialleonardo, E., Facchinetti, A., and Carnevale, M. (2023). A Speed-Dependent Condition Monitoring System for Track Geometry Estimation Using Inertial Measurements, National Research Council Canada.10.1007/978-3-031-66971-2_124 ↗
11. Monitoring Vertical Track Irregularity from In-Service Railway Vehicles10.1243/0954409JRRT65 ↗
12. Track Geometry Estimation from Car-Body Vibration10.1080/00423114.2014.889836 ↗
13. A Measurement System for Quick Rail Inspection and Effective Track Maintenance Strategy10.1016/j.ymssp.2006.02.007 ↗
14. Bruni, S., Carnevale, M., Cazzulani, G., Di Gialleonardo, E., and La Paglia, I. (2025). AI for Rail Infrastructure and Maintenance. Handbook on Digital Twin and Artificial Intelligence Techniques for Rail Applications, CRC Press.10.1201/9781003492146-15 ↗
15. Hadj-Mabrouk, H. (2025). Contributions and Limitations of AI and Machine Learning in Railway Operations, Maintenance, and Safety: A Literature Review. Proc. Inst. Mech. Eng. Part O J. Risk Reliab., 1748006X251364324.10.1177/1748006X251364324 ↗
16. Helmi, W., Bridgelall, R., and Askarzadeh, T. (2024). Remote Sensing and Machine Learning for Safer Railways: A Review. Appl. Sci., 14.10.3390/app14093573 ↗
17. Deep Multitask Learning for Railway Track Inspection10.1109/TITS.2016.2568758 ↗
18. Semi-Supervised Rail Defect Detection from Imbalanced Image Data
19. Collart-Dutilleul, S., Lecomte, T., and Romanovsky, A. (2019, January 4–6). The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey. Proceedings of the Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification, Lille, France.10.1007/978-3-030-18744-6 ↗
20. Alfi, S., and Bruni, S. (2008, January 17–20). Estimation of Long Wavelength Track Irregularities from on Board Measurement. Proceedings of the 2008 4th IET International Conference on Railway Condition Monitoring, Derby, UK.10.1049/ic:20080323 ↗
21. Alfi, S., De Rosa, A., and Bruni, S. (2016, January 27–28). Estimation of Lateral Track Irregularities from On-Board Measurement: Effect of Wheel-Rail Contact Model. Proceedings of the 7th IET Conference on Railway Condition Monitoring 2016 (RCM 2016), Birmingham, UK.10.1049/cp.2016.1205 ↗
22. Estimation of Lateral and Cross Alignment in a Railway Track Based on Vehicle Dynamics Measurements10.1016/j.ymssp.2018.06.041 ↗
23. Monitoring of Lateral and Cross Level Track Geometry Irregularities Through Onboard Vehicle Dynamics Measurements Using Machine Learning Classification Algorithms10.1177/0954409720906649 ↗
24. Nielsen, J.C.O., Anderson, D., Gautier, P.-E., Iida, M., Nelson, J.T., Thompson, D., Tielkes, T., Towers, D.A., and de Vos, P. (2016, January 12–16). Monitoring Rail Condition Based on Sound and Vibration Sensors Installed on an Operational Train. Proceedings of the Noise and Vibration Mitigation for Rail Transportation Systems, Terrigal, Australia.
25. An Introduction to Observers10.1109/TAC.1971.1099826 ↗
26. Monitoring Lateral Track Irregularity from In-Service Railway Vehicles10.1243/0954409JRRT64 ↗
27. A Mixed Filtering Approach for Track Condition Monitoring Using Accelerometers on the Axle Box and Bogie10.1109/TIM.2011.2170377 ↗
28. Ding, S.X. (2013). Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools, Springer. Advances in Industrial Control.10.1007/978-1-4471-4799-2 ↗
29. Unknown Input Observer-Based Fault Diagnosis of Speed Sensors in Dual Clutch Transmission10.1177/09544070221095286 ↗
30. Unknown Input Observer Design for Fault Sensor Estimation Applied to Induction Machine10.1016/j.matcom.2018.09.018 ↗
31. Chen, J., and Patton, R.J. (1999). Robust Residual Generation Using Unknown Input Observers. Robust Model-Based Fault Diagnosis for Dynamic Systems, Springer.10.1007/978-1-4615-5149-2_3 ↗
32. La Paglia, I., Santelia, M., Alfi, S., Di Gialleonardo, E., and Facchinetti, A. (2024, January 1–5). An Application of the Unknown Input Observer Algorithm for the Identification of Vertical Railway Track Irregularity. Proceedings of the Railways 2024—The Sixth International Conference on Railway Technology: Research, Development and Maintenance, Prague, Czech Republic.10.4203/ccc.7.19.5 ↗
33. Manchester Benchmarks for Rail Vehicle Simulation10.1080/00423119808969454 ↗
34. UIC (1971). ORE Power Spectral Density of Track Irregularities. Part 1: Definitions, Conventions and Available Data, UIC Railway Publications. Question ORE C116-Rp1 1971.