基于车载振噪多源数据融合的地铁波磨快速检测方法

高天赐; 江乐鹏; 从建力; 王源; 刘晓舟; 朱家松; 罗钦; 王平

doi:10.3969/j.issn.0258-2724.20240121

基于车载振噪多源数据融合的地铁波磨快速检测方法

doi: 10.3969/j.issn.0258-2724.20240121

高天赐^{1, 2,},
江乐鹏³,
从建力⁴,
王源⁵,
刘晓舟²,
朱家松^6, ,,
罗钦²,
王平⁷

1.
深圳大学应用技术学院，广东深圳 518060
2.
深圳技术大学城市交通与物流学院，广东深圳 518118
3.
华东交通大学交通运输工程学院，江西南昌 330013
4.
中国铁道科学研究院集团有限公司基础设施检测研究所，北京 100081
5.
深圳市埃伯瑞科技有限公司，广东深圳 518052
6.
深圳大学土木与交通工程学院，广东深圳 518060
7.
西南交通大学高速铁路线路工程教育部重点实验室，四川成都 610031

基金项目: 国家自然科学基金项目（52008198）；广东省普通高校创新团队（2022KCXTD027）；广东省重点建设学科科研能力提升项目（2021ZDJS108）

详细信息

作者简介:
高天赐（1995—），男，博士后，博士，研究方向为铁路智能检测，E-mail：gaotianci@sztu.edu.cn

通讯作者:
朱家松（1975—），男，教授，研究方向为智能交通系统，E-mail： zjsong@szu.edu.cn

中图分类号: U213.2
计量
- 文章访问数: 84
- HTML全文浏览量: 29
- PDF下载量: 27
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-12
- 修回日期: 2024-07-01
- 网络出版日期: 2025-07-23

Rapid Detection Method for Rail Corrugation in Metro Lines Based on Data Fusion of Train-Borne Vibration and Noise

1.
College of Applied Sciences, Shenzhen University, Shenzhen 518060, China
2.
College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China
3.
School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China
4.
Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
5.
Eborail Technology Co., Ltd., Shenzhen 518052, China
6.
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
7.
Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu 610031, China

摘要

摘要:
实现地铁钢轨波磨的快速辨识与精准定位，可以方便地铁运维部门制定合理的维修计划，对于节省地铁工务作业具有重要意义. 本研究利用低成本的便携式车载传感终端，快速检测地铁列车在全线的车体振动与噪声数据；在此基础之上，考虑到地下环境中难以获取稳定的GPS信号，采用基于纵向加速度二次积分、车体摇头角速度与线路平面曲率匹配的多源数据融合方法，实现对振噪检测数据的精准里程定位；进一步，结合现场波磨检测结果，提出了波深指数的振噪声纹特征，辨识钢轨波磨，并利用分位数回归建立起波深指数与波磨波深之间的关联关系. 研究结果表明：基于波深指数的波磨辨识和定位结果与现场结果一致，波磨主要波长集中在40 mm，并且随着噪声波深指数的增大，钢轨波磨的波深呈现出“扇形”式增长，符合分位数回归特征，可进一步估算在不同分位数下波噪管理标准.
- 钢轨波磨 /
- 多源数据融合 /
- 里程定位 /
- 波深指数 /
- 分位数回归
Abstract:
The rapid identification and accurate localization of rail corrugation in metro lines are of significant importance for the maintenance departments of metros to formulate reasonable maintenance plans, thereby saving considerable efforts in metro operational works. In this study, low-cost, portable, and vehicle-mounted sensing terminals were utilized to detect the vibration and noise of metro trains across the entire line. Given the difficulty in obtaining stable GPS signals in underground environments, a multi-source data fusion method based on the secondary integration of longitudinal acceleration, the yaw rate of the vehicle body, and the matching with the line’s planar curvature was adopted to achieve precise mileage localization of the detected vibration and noise data. Building upon this foundation and in conjunction with on-site corrugation detection results, a vibrational-noise feature of the wave depth index for identifying rail corrugation was proposed. Furthermore, by utilizing quantile regression, a correlation between the wave depth index and corrugation depth was established. The findings indicate that the corrugation identification and localization results based on the wave depth index are consistent with on-site observations, with the primary wavelength of corrugation concentrated around 40 mm. Additionally, as the wave depth index increases, the corrugation depth exhibits a “fan-shaped” growth pattern, consistent with the characteristics of quantile regression, enabling the estimation of corrugation noise management thresholds at different quantile levels.
- rail corrugation /
- multi-source data fusion /
- mileage-based localization /
- wave depth index /
- quantile regression

HTML全文

图 1 便携式智能传感终端及其安装位置

Figure 1. Portable intelligent sensing terminal and its installation position

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图 2 钢轨波磨测试

Figure 2. Field experiment of rail corrugation

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图 3 振噪检测数据里程定位流程

Figure 3. Flowchart of mileage-based localization using vibration and noise data

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图 4 曲线主点处初始里程的偏差估计

Figure 4. Estimation of initial mileage deviation at main points of curves

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图 5 振噪数据里程标定效果

Figure 5. Mileage-based localization using vibration and noise data

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图 6 钢轨波磨与车内振噪检测数据沿里程分布

Figure 6. Rail corrugation and in-car vibration/noise data distribution along mileage

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图 7 车内噪声时频分析结果

Figure 7. Time-frequency analysis results of in-car noise

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图 8 基于波深指数的钢轨波磨辨识结果

Figure 8. Rail corrugation identification results based on wave depth index

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图 9 噪声波深指数与钢轨波磨关系

Figure 9. Relationship between wave depth index and rail corrugation

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