| 注册
首页|期刊导航|北京交通大学学报|基于轨道板振动加速度的钢轨振动加速度反演估计与现场验证

基于轨道板振动加速度的钢轨振动加速度反演估计与现场验证

何庆 曾楚琦 王启航 付彬 吴军 王平

北京交通大学学报2024,Vol.48Issue(1):74-86,13.
北京交通大学学报2024,Vol.48Issue(1):74-86,13.DOI:10.11860/j.issn.1673-0291.20230041

基于轨道板振动加速度的钢轨振动加速度反演估计与现场验证

Inversion estimation and field verification of rail vibration acceleration based on vibration acceleration of track slabs

何庆 1曾楚琦 2王启航 1付彬 1吴军 3王平1

作者信息

  • 1. 西南交通大学高速铁路线路工程教育部重点实验室 成都 610031||西南交通大学土木工程学院,成都 610031
  • 2. 西南交通大学高速铁路线路工程教育部重点实验室 成都 610031||西南交通大学土木工程学院,成都 610031||中国电建集团中南勘测设计研究院有限公司城建交通工程院,长沙 410014
  • 3. 中国铁路成都局集团有限公司涪陵工务段,重庆 610000
  • 折叠

摘要

Abstract

To explore the spatiotemporal correlation patterns between high-speed railway track slabsand rails, this paper proposes a Variational Mode Decomposition-Transformer (VMD-T) inversion model, which estimates rail vibration acceleration from decomposed track slab vibration accelerationdata. Firstly, the data undergoes preprocessing, including vibration endpoint detection using a dual-threshold method to separate the vibration signals from silent and interference signals. The extracted vibration signals are integrated and fed into the VMD-T model. Subsequently, the VMD model breaks down the track slab vibration acceleration data into various sub-modes. A grid search identifies the sub-modes with the highest correlation coefficients with rail vibration acceleration, aiming to sim-plify the original dataset and reduce its non-stationarity, thereby enhancing the Transformer model's feature extraction capabilities. Following this, the Transformer model undertakes inversion estimation training on the identified track slab vibration acceleration sub-modes and rail vibration acceleration data. Finally, the model's utility is demonstrated through inversion estimation trials using actual vibra-tion acceleration data from the track slabs and rails of a specific intercity high-speed railway. Field tests on high-speed rail reveal that, compared to a standalone Transformer model, the VMD-T model con-siderably improves in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the determination coefficient (R2_score) by approximately 20%, 11%, and 48.1% respectively, indi-cating a stronger capability in feature learning and inversion estimation. This study preliminarily achieves non-contact estimation monitoring of rail vertical vibration acceleration magnitudes.

关键词

高速铁路/变分模态分解/Transformer模型/轨道板/钢轨/振动加速度

Key words

high-speed railway/variational mode decomposition/Transformer model/track slab/rail/vibration acceleration

分类

交通运输

引用本文复制引用

何庆,曾楚琦,王启航,付彬,吴军,王平..基于轨道板振动加速度的钢轨振动加速度反演估计与现场验证[J].北京交通大学学报,2024,48(1):74-86,13.

基金项目

国家自然科学基金(52372400,52068052,52388102)National Natural Science Foundation of China(52372400,52068052,52388102) (52372400,52068052,52388102)

北京交通大学学报

OA北大核心CSTPCD

1673-0291

访问量0
|
下载量0
段落导航相关论文