Scientific articles

We are pleased to share with you all the scientific articles that have been produced so far within the FP3-IAM4Rail project.

You will find below the links to access each of these publications. We hope you enjoy reading them and discovering all the advancements and innovations in the field of railway technology that are being developed within the FP3 project.

These publications reflect the collaborative effort and expertise of all the partners involved, aiming to contribute valuable knowledge to the railway research community. We encourage you to explore the findings and insights presented, and we look forward to continuing to share future outcomes of our work:

1. ProRail. Paper on analysis of transition zones using ABA measurements. S. Unsiwilai, L. Wang, A. Núñez, and Z. Li, “Multiple-axle box acceleration measurements at railway transition zones”. Measurement, Volume 213, May 2023, 112688:  https://doi.org/10.1016/j.measurement.2023.112688  
2. ProRail. Review paper TUDelft+PRORAIL+DB on AI technologies for Infrastructure. W. Phusakulkajorn, A. Núñez, H. Wang, A. Jamshidi, A. Zoeteman, B. Ripke, R. Dollevoet, B. De Schutter and Z. Li, “Artificial intelligence in railway infrastructure: current research, challenges and future opportunities”. Intelligent Transportation Infrastructure, Volume 2, 2023, liad016: https://doi.org/10.1093/iti/liad016 
3. DLR. Paper “Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations”: https://www.mdpi.com/1424-8220/24/2/477 
4. ProRail. Paper on fundamentals of AI (physics informed NN) towards simulation of beams, T. Kapoor, H. Wang, A. Núñez, and R. Dollevoet, “Physics-informed neural networks for solving forward and inverse problems in complex beam systems.” IEEE Transactions on Neural Networks and Learning Systems, Volume 35, Issue 5, Pages: 5981-5995, May 2024. Intelligence, Volume 133, Part A, July 2024, 108085: Paper: https://doi.org/10.1109/TNNLS.2023.3310585. Codes: https://github.com/taniyakapoor/PINNs_beam/tree/master  
5. ProRail. Key paper on the combined use of LDV and ABA under controlled/laboratory conditions, proven TRL4 level of the technology. Y. Zeng, A. Núñez, Z. Li, “Measuring transfer functions of tracks structures in a test rig with laser Doppler vibrometer and accelerometers on a moving vehicle.” Mechanical Systems and Signal Processing, Volume 214, May 2024, 111392: https://doi.org/10.1016/j.ymssp.2024.111392  
6. ProRail. Paper on fundamentals of AI (transfer learning and causal Physics informed NN) towards simulation of beams, T. Kapoor, H. Wang, A. Núñez and R. Dollevoet, “Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations”. Engineering Applications of Artificial Intelligence, Volume 133, Part A, July 2024, 108085: ‘Paper: https://doi.org/10.1016/j.engappai.2024.108085. Codes: https://github.com/taniyakapoor/Causal-PINN-for-beam/tree/second  
7. ProRail. Scientific article called “Enhanced Vertical Railway Track Quality Index with Dynamic Responses from Moving Trains”. Siwarak Unsiwilai, Wassamon Phusakulkajorn, Chen Shen, Arjen Zoeteman, Rolf Dollevoet, Alfredo Núñez, Zili Li. 15 October 2024, e38670. https://doi.org/10.1016/j.heliyon.2024.e38670; Enhanced vertical railway track quality index with dynamic responses from moving trains – ScienceDirect 
8. ProRail. Paper “Vertical dynamic measurements of a railway transition zone: a case study in Sweden”. Siwarak Unsiwilai, Chen Shen, Yuanchen Zeng, Li Wang, Alfredo Núñez & Zili Li. 21 February 2024. Vertical dynamic measurements of a railway transition zone: a case study in Sweden | Journal of Civil Structural Health Monitoring 
9. ProRail. Paper “Enhanced vertical railway track quality index with dynamic responses from moving trains”. Siwarak Unsiwilai ∙ Wassamon Phusakulkajorn ∙ Chen Shen ∙ Arjen Zoeteman ∙ Rolf Dollevoet ∙ Alfredo Núñez∙ Zili Li. October 15, 2024. Enhanced vertical railway track quality index with dynamic responses from moving trains: Heliyon
10. ProRail. Paper “A Train-Borne Laser Vibrometer Solution Based on Multisignal Fusion for Self-Contained Railway Track Monitoring”. Yuanchen Zeng; Alfredo Núñez; Rolf Dollevoet; Arjen Zoeteman; Zili Li February 2025. A Train-Borne Laser Vibrometer Solution Based on Multisignal Fusion for Self-Contained Railway Track Monitoring | IEEE Journals & Magazine | IEEE Xplore
11. ProRail. Paper “Neural Differential Equation-Based Two-Stage Approach for Generalization of Beam Dynamics”. Taniya Kapoor; Hongrui Wang; Anastasios Stamou; Kareem El Sayed; Alfredo Núñez; Daniel M. Tartakovsky. March 2025. Neural Differential Equation-Based Two-Stage Approach for Generalization of Beam Dynamics | IEEE Journals & Magazine | IEEE Xplore
12. CEIT. Paper “A systematic review of acceleration-based estimation of railway track quality”. Adrián Sansiñena, Borja Rodríguez de Arana, Saioa Arrizabalaga. 26 Mar 2025. A systematic review of acceleration-based estimation of railway track quality: Vehicle System Dynamics: Vol 0, No 0 
13. Fatigue Crack Propagation on Uniaxial Loading of Biaxially Predeformed Pearlitic Rail Steel
    https://doi.org/10.3390/met13101726
14. Partial safety factor calibration using surrogate models: An application for running safety of ballasted high-speed railway bridges Partial safety factor calibration using surrogate models: An application for running safety of ballasted high-speed railway bridges – ScienceDirect
15. Evaluations and enhancements of fatigue crack initiation criteria for steels subjected to large shear deformations
    Evaluations and enhancements of fatigue crack initiation criteria for steels subjected to large shear deformations – ScienceDirect
16. Measuring transfer functions of track structures in a test rig with laser Doppler vibrometer and accelerometers on a moving vehicle
    https://doi.org/10.1016/j.ymssp.2024.111392
17. Spiking neural network with time-varying weights for rail squat detection Spiking neural network with time-varying weights for rail squat detection – ScienceDirect
18. Robust health indicator and rankings for railway point machines using motor current curves https://doi.org/10.1093/iti/liag005
19. An Integrated Machine Learning and Optimization Framework for Railway Track Quality Assessment: Application to Longitudinal Level An Integrated Machine Learning and Optimization Framework for Railway Track Quality Assessment: Application to Longitudinal Level
20. Multiple-axle box acceleration measurements at railway transition zones Multiple-axle box acceleration measurements at railway transition zones – ScienceDirect 
21. A digital twin for analysing dynamic vehicle responses
    electronic library – Ein digitaler Zwilling für die Analyse von dynamischen Fahrzeugreaktionen. Link to publication on DLR Electronic Library: https://elib.dlr.de/211547/
22. Proceedings of the Seventh International Conference on Railway Technology: Research, Development and Maintenance
    Computational Technology Resources – CCC – Paper
23. Multimodal Data Analysis for Railway Switch Monitoring: Point Machine Motor Currents, Track Geometry, and Maintenance Records Multimodal Data Analysis for Railway Switch Monitoring: Point Machine Motor Currents, Track Geometry, and Maintenance Records
24. . Detecting Faults in Electric Motors Based on Current and Rotor Speed Measurement Using a Naive Bayesian Classifier
    Detecting Faults in Electric Motors Based on Current and Rotor Speed Measurement Using a Naive Bayesian Classifier | IEEE Conference Publication | IEEE Xplore
25. DUDU-BLDC: Dataset for Diagnostics of Brushless DC Motors with Degrading Magnets DUDU-BLDC: Dataset for Diagnostics of Brushless DC motors with Degrading Magnets | IEEE Conference Publication | IEEE Xplore
26. Improving BLDC Motor Fault Detection via KDE-Based Bayesian Data Fusion Improving BLDC Motor Fault Detection via KDE-Based Bayesian Data Fusion | IEEE Conference Publication | IEEE Xplore
27. Limitations of Rolling RMS for Bayesian Functional Classification of Induction Motor Faults Limitations of Rolling RMS for Bayesian Functional Classification of Induction Motor Faults | IEEE Conference Publication | IEEE Xplore
28. Bayesian Changepoint Detection for Smart Sensing of Battery Degradation: Cycle-Level Health Indicators and PyMC Implementation
    Bayesian Changepoint Detection for Smart Sensing of Battery Degradation: Cycle-Level Health Indicators and PyMC Implementation | IEEE Conference Publication | IEEE Xplore
    Version on arXiv: https://arxiv.org/pdf/2606.26363v1
29. Bayesian and Classical Feature Ranking for Interpretable BLDC Fault Diagnosis
    Bayesian and Classical Feature Ranking for Interpretable BLDC Fault Diagnosis | IEEE Conference Publication | IEEE Xplore
    Version on arXiv: https://arxiv.org/html/2603.14509v1
30. Low-Data Predictive Maintenance of Railway Station Doors and Elevators Using Bayesian Proxy Flow Modeling
    Low-Data Predictive Maintenance of Railway Station Doors and Elevators Using Bayesian Proxy Flow Modeling | IEEE Conference Publication | IEEE Xplore
    Version on arXiv: https://arxiv.org/html/2603.14384v1
31. A Constrained Multi-Resolution Spatial Trend Estimation Framework for Commercial Rent Monitoring: A Synthetic Study Within a Decision Support Architecture
    A Constrained Multi-Resolution Spatial Trend Estimation Framework for Commercial Rent Monitoring: A Synthetic Study Within a Decision Support Architecture | IEEE Conference Publication | IEEE Xplore
32. Optimization of Predictive Maintenance Schedules Under Uncertainty: A Scenario-Based Theoretical Framework
    Optimization of Predictive Maintenance Schedules Under Uncertainty: A Scenario-Based Theoretical Framework | IEEE Conference Publication | IEEE Xplore
    Version on arXiv: https://arxiv.org/html/2605.30222v1
33. Schedule-Based Urban Accessibility Relative to a Railway Node Using GTFS, OpenStreetMap, and Triangulated Travel-Time Isochrones
    Schedule-Based Urban Accessibility Relative to a Railway Node Using GTFS, OpenStreetMap, and Triangulated Travel-Time Isochrones | IEEE Conference Publication | IEEE Xplore
34. Stable and Compact Diagnostic Signatures for Demagnetization-Related Faults in BLDC/PMSM Drives: Evidence from Two Measurement Campaigns
    Stable and Compact Diagnostic Signatures for Demagnetization-Related Faults in BLDC/PMSM Drives: Evidence from Two Measurement Campaigns

Disclaimer that applies to each and every one of the scientific articles published here:

“Funded by the European Union. Views and opinion expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Europe’s Rail Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them. The project FP3-IAM4Rail is supported by the Europe’s Rail Joint Undertaking and its members.”