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
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