Dr. Yuanchen Zeng is a Postdoc Researcher at the Section of Railway Engineering, Delft University of Technology. His research focuses on train-borne monitoring and assessment of rail infrastructure, especially the development and application of Laser Doppler Vibrometer on a moving platform. He works within Europe’s Rail flagship project IAM4RAIL (Holistic and integrated asset management for Europe’s rail system), TKI-HTSM projects MemoLDV and 3D-LDVom, and ProRail project RESET (Reliable embankments for safe expansion in rail traffic).

Yuanchen Zeng obtained his PhD degree from Delft University of Technology in 2023 with a thesis on the topic of Monitoring dynamic properties of railway tracks using train-borne vibrometer measurement. His thesis was selected as the best PhD thesis by European Railway Research Advisory Council (ERRAC) in 2024.

Yuanchen Zeng is involved in the development and teaching of several MSc courses on vehicle-track dynamics and structural health monitoring. He has successfully supervised 3 BSc theses and 2 MSc theses (both students graduated with cum laude) at TU Delft. He is supervising PhD 4 of the project RESET and PhD 2 of the project 3D-LDVom.

Yuanchen Zeng obtained his Bachelor's degree in Mechatronics Engineering from Zhejiang University in 2016. Then, he completed his Master-PhD combined program at the State Key Laboratory of Traction Power, Southwest Jiaotong University in 2022. His research focused on the monitoring, prognostics, and maintenance of high-speed train wheels. Some key publications from his previous research are listed below.

1. Y. Zeng, D. Song, W. Zhang, et al. An optimal life cycle reprofiling strategy of train wheels based on Markov decision process of wheel degradation. IEEE Transactions on Intelligent Transportation Systems. 2022, 23(8): 10354 - 10364.
2. Y. Zeng, D. Song, W. Zhang, et al. Physics-based data-driven interpretation and prediction of rolling contact fatigue damage on high-speed train wheels. Wear. 2021, 484: 203993.
3. Y. Zeng, D. Song, W. Zhang, et al. A new physics-based data-driven guideline for wear modelling and prediction of train wheels. Wear. 2020, 456: 203355.
4. Y. Zeng, D. Song, W. Zhang, et al. Risk assessment of wheel polygonization on high-speed trains based on Bayesian networks. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2021, 235(2): 182-192.
5. Y. Zeng, D. Song, W. Zhang, et al. Influence of different railway lines on wheel damage of high-speed trains: Data-driven modelling and prediction. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2022: 1748006X221122032.
6. Y. Zeng, W. Zhang, D. Song, et al. A new strategy for hunting alarm and stability evaluation for railway vehicles based on nonlinear dynamics analysis. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 2020, 234(1): 54-64.