Effects of Rail Vehicle Dynamics Modelling Choices on Machine Learning Analysis

被引：0
|
作者：
Licciardello, Riccardo [1 ]
Kaviani, Nadia [1 ]
Arabani, Sina S. [1 ]
机构：
[1] SAPIENZA Univ Rome, DICEA Dept Civil Bldg & Environm Engn, I-00184 Rome, Italy
来源：
ADVANCES IN DYNAMICS OF VEHICLES ON ROADS AND TRACKS III, VOL 1, IAVSD 2023 | 2025年
关键词：
rail inclination; equivalent conicity; machine learning; multi-body simulation;
D O I：
10.1007/978-3-031-66971-2_26
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Rail inclination is a well-known important track design parameter. It may have a measurable influence on the running dynamic behaviour of railway vehicles, as it affects equivalent conicity. Their effects are clearly visible when training Machine Learning (ML) algorithms for different purposes. This has been observed in on-going research regarding the detection of rail alignment using computer vision for in-service condition-monitoring. This paper briefly summarises the condition-monitoring research, and goes into detail regarding the effects of inclination and conicity explained from a vehicle dynamics viewpoint.
引用
收藏
页码：241 / 249
页数：9
相关论文
共 50 条
  • [31] A non-linear rubber spring model for rail vehicle dynamics analysis
    Berg, M
    VEHICLE SYSTEM DYNAMICS, 1998, 30 (3-4) : 197 - 212
  • [32] Modelling and analysis of the dynamics of a tilting three-wheeled vehicle
    Edelmann, Johannes
    Ploechl, Manfred
    Lugner, Peter
    MULTIBODY SYSTEM DYNAMICS, 2011, 26 (04) : 469 - 487
  • [33] Modelling and analysis of the dynamics of a tilting three-wheeled vehicle
    Johannes Edelmann
    Manfred Plöchl
    Peter Lugner
    Multibody System Dynamics, 2011, 26 : 469 - 487
  • [34] Dynamics modelling and vibration characteristics of urban rail vehicle axle-box bearings with the cage crack
    Yu, Yaoxiang
    Guo, Liang
    Chen, Zaigang
    Gao, Hongli
    Shi, Zhifeng
    Zhang, Guoli
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 205
  • [35] Machine Learning for GNSS Performance Analysis and Environment Characterization in Rail domain
    Neri, Alessandro
    Ruggeri, Agostino
    Vennarini, Alessia
    Coluccia, Andrea
    PROCEEDINGS OF THE 33RD INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS+ 2020), 2020, : 3561 - 3566
  • [36] Strength Analysis of the Box of On-line Cleaning Machine for Rail Transit Vehicle Shell
    Tang Youming
    Lv Changming
    Lv Na
    Zha Xinyu
    Ou-Yang Liange
    2019 11TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2019), 2019, : 208 - 211
  • [37] Pushing the frontiers in climate modelling and analysis with machine learning
    Eyring, Veronika
    Collins, William D.
    Gentine, Pierre
    Barnes, Elizabeth A.
    Barreiro, Marcelo
    Beucler, Tom
    Bocquet, Marc
    Bretherton, Christopher S.
    Christensen, Hannah M.
    Dagon, Katherine
    Gagne, David John
    Hall, David
    Hammerling, Dorit
    Hoyer, Stephan
    Iglesias-Suarez, Fernando
    Lopez-Gomez, Ignacio
    Mcgraw, Marie C.
    Meehl, Gerald A.
    Molina, Maria J.
    Monteleoni, Claire
    Mueller, Juliane
    Pritchard, Michael S.
    Rolnick, David
    Runge, Jakob
    Stier, Philip
    Watt-Meyer, Oliver
    Weigel, Katja
    Yu, Rose
    Zanna, Laure
    NATURE CLIMATE CHANGE, 2024, : 916 - 928
  • [38] Comparative analysis of machine learning techniques for enhanced vehicle tracking and analysis
    Rani, Seema
    Dalal, Sandeep
    Transportation Engineering, 2024, 18
  • [39] Integrated machine learning and mechanistic modelling of Metabolic Syndrome development and dynamics
    Van Riel, Natal
    EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, 2019, 49 : 40 - 41
  • [40] WAVE DYNAMICS RUN-UP MODELLING: MACHINE LEARNING APPROACH
    Vileti, Vinicius L.
    Ersdal, Svein
    PROCEEDINGS OF ASME 2024 43RD INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, OMAE2024, VOL 5B, 2024,
12345