铁道车辆车轮扁疤故障检测技术综述

曾京; 彭莘宇; 汪群生; 张浩; 梁松康

doi:10.19818/j.cnki.1671-1637.2022.02.001

铁道车辆车轮扁疤故障检测技术综述

doi: 10.19818/j.cnki.1671-1637.2022.02.001

西南交通大学牵引动力国家重点实验室，四川成都 610031

基金项目:

国家自然科学基金项目 61960206010

国家自然科学基金项目 52102441

牵引动力国家重点实验室自主课题 2022TPL-T10

牵引动力国家重点实验室自主课题 2019TPL-T18

详细信息

作者简介:
曾京(1963-), 男, 湖南涟源人, 西南交通大学教授, 工学博士, 从事车辆系统动力学与控制研究

中图分类号: U270.331
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出版历程
- 收稿日期: 2021-11-04
- 刊出日期: 2022-04-25

Review on detection technologies of railway vehicle wheel flat fault

State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Funds:

National Natural Science Foundation of China 61960206010

National Natural Science Foundation of China 52102441

Independent Project of State Key Laboratory of Traction Power 2022TPL-T10

Independent Project of State Key Laboratory of Traction Power 2019TPL-T18

More Information

Author Bio:
ZENG Jing(1963-), male, professor, PhD, zeng@swjtu.edu.cn

摘要

摘要: 从铁道车辆车轮扁疤对轨道的冲击效应及其对车辆零部件造成的损伤出发，系统梳理了检测车轮扁疤的多种方案，对各类车轮扁疤故障检测方法特点进行了讨论，对比了不同检测方法的优缺点，对车轮扁疤故障检测技术体系的发展方向进行了预测。分析结果表明：车轮扁疤故障检测技术可分为车载检测法和地面检测法，其中地面检测法运用较为广泛；现阶段较为成熟的车轮扁疤检测技术按检测手段可主要分为轮轨冲击检测法、超声波检测法、噪声检测法、踏面位移法、振动加速度检测法、图像检测法、光学检测法、轨道电路中断法等；近年来，随着科学技术的发展，又涌现了如多普勒效应法、超声波回声定位法等；随着现代智能算法的进步，应用神经网络等智能算法对设备进行故障识别训练能大大简化设备开发进程和结构，智能算法或将成为车轮扁疤故障识别的主要发展方向；随着时间推移，检测设备的多故障集成化趋势越发明显，多故障检测集成化与功能多样化已是智能化检测设备发展的重要方向之一；未来，操作系统方面的提升也将主要集中于平台的人性化和智能化方面；检测体系建议由正线实时监测、车辆段入库精准检测、数据信息化平台三部分组成，未来发展方向会集中在装置简易化、算法精准化与操作智能化等方面。
- 铁道车辆 /
- 车轮扁疤 /
- 综述 /
- 检测技术 /
- 智能化 /
- 监测
Abstract: From the impact effect of wheel flat of railway vehicle on track and its damage to vehicle parts, several schemes for the wheel flat detection were systematically combed out.The characteristics of various kinds of wheel flat fault detection methods were discussed, the advantages and disadvantages of different methods were compared, and the development trend of the system for wheel flat fault detection technologies was predicted. Analysis results reveal that the wheel flat fault detection technologies can be divided into the vehicle-mounted detection method and wayside detection method, among which the wayside detection method is widely used. At present, the relatively mature wheel flat detection technologies are mainly divided into the wheel and rail impact detection method, ultra-sonic detection method, noise detection method, wheel tread displacement detection method, vibration acceleration detection method, image detection method, optical detection method, track circuit interruption method and so on.In recent years, with the development of science and technology, methods such as the Doppler effect method, ultrasonic echolocation method and so on have emerged.With the progress of modern intelligent algorithms, intelligent algorithms such as the neural networks are employed to the train equipment for the fault identification, which can greatly simplify the equipment development process and device structure. Therefore, intelligent algorithms may become the main development direction of wheel flat fault identification. As time goes by, the trend of multi-fault integration of detection equipment becomes more prominent, and multi-fault detection integration and functional diversification have become one of the important directions in the development of intelligent detection equipment. In the future, the improvements in operating systems will also focus on the humanization and intelligence of platforms. Suggestions on the detection system are put forward from three aspects, namely, real-time monitoring of the operation line, accurate detection of depot entry, and information-based data platforms. Future development should emphasize simple devices, accurate algorithms, and intelligent operation. 3 tabs, 22 figs, 79 refs.
- railway vehicle /
- wheel flat /
- review /
- detection technology /
- intelligence /
- monitoring

HTML全文

图 1 2018年神朔铁路车辆故障报警统计

Figure 1. Statistics of vehicle fault alarms of Shen-Shuo Railway vehicles in 2018

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图 2 车轮扁疤理论计算

Figure 2. Theoretical calculation of wheel flat

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图 3 测力轮对的应变片粘贴

Figure 3. Strain gauge sticking of force measuring wheelset

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图 4 车轮故障声学检测系统关键部件

Figure 4. Key components of wheel fault acoustic detection system

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图 5 车辆振动加速度测试

Figure 5. Vehicle vibration acceleration measurement

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图 6 TPDS轮轨力测试原理^[32]

Figure 6. TPDS wheel-rail force measuring principle ^[32]

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图 7 TODS轮轨力测试与连续原理^[42]

Figure 7. TODS wheel-rail force test and continuous principle

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图 8 轨边声学检测系统

Figure 8. Trackside acoustic detection system

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图 9 轨道振动加速度测试

Figure 9. Track vibration acceleration measurement

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图 10 振动加速度传感器安装位置

Figure 10. Installation positions of vibration acceleration sensors

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图 11 钢轨冲击振动加速度测试信号

Figure 11. Test signals of rail shock vibration acceleration

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图 12 铁路车轮超声B型成像检测

Figure 12. Ultrasonic B-mode imaging detection of railway wheels

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图 13 探头组阵列和传感器位置

Figure 13. Probe group array and sensor locations

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图 14 轮对动态检测装置

Figure 14. Wheelset dynamic detection device

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图 15 位移检测法原理

Figure 15. Principle of displacement detection method

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图 16 轮对故障动态监测系统安装位置

Figure 16. Installation position of wheelset fault dynamic monitoring system

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图 17 CCD车轮外形检测系统原理

Figure 17. Principle of CCD wheel shape detection system

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图 18 车轮踏面损伤检测系统结构

Figure 18. Structure of wheel tread damage detection system

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图 19 光学检测法原理

Figure 19. Principle of optical detection method

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图 20 轨道电路中断法检测原理

Figure 20. Detection principle of track circuit interruption method

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图 21 车轮故障检测论文数量

Figure 21. Numbers of published articles on wheel fault detection

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图 22 超声波回声定位法

Figure 22. Ultrasonic echolocation method

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表 1 车载检测法的优势与不足

Table 1. Advantages and disadvantages of vehicle-mounted detection methods

检测方法	优点	不足
轮轨力检测法	技术较为成熟，车辆低速段识别精准	成本高昂，整列车采用测力轮对基本不太现实，高速时轮轨力冲击与扁疤故障线性化程度低
超声波检测法	故障识别精准，兼顾车轮其他故障识别	成本较高，仅适用于单个车轮，无法对全部车辆进行监测
声学检测法	结构简单，对车速没有限制要求	识别精度一般，抗干扰性较低，仅适用单侧转向架车轮
振动检测法	结构相对简单，安装简便，识别精准度高，安装于车体时可检测单个车辆所有车轮	暂时无法解决车载法的检测范围局限性，即无法对所有车辆进行监测

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表 2 地面检测法的优势与不足

Table 2. Advantages and disadvantages of wayside detection methods

检测方法	优点	不足
轮轨力检测法	车辆低速时识别精准，轮轨力测试手段成熟，可靠性高	高速段故障与轮轨力冲击线性化程度低，需借助辅助手段识别；连续轮轨力采集方法复杂，一定程度上提高了设备成本
声学检测法	结构简单，成本较低，对车速没有限制要求	识别精度一般，抗干扰性较低
地面振动检测法	结构简单，成本低	信号强度随车辆位置变化有不同，限制了检测精度，常用于辅助识别
超声波检测法	故障识别精准，且兼顾其他车轮故障	造价高昂，对车速有限制要求，无法安装于正线
位移检测法	结构相对简单，识别精准度较高	需限制通过车速，无法安装于正线
图像检测法	结构简单，成本较低，对车速没有限制要求	识别精度受环境影响较大，无法识别扁疤长度
光学检测法	测试准确，可靠性高，对车速有一定限制	结构较为复杂，精度一般，受雨雪等环境影响
轨道电路中断法	车辆高速通过时识别精准度较高	结构复杂，需要对轨道进行改造，仅能检测单侧钢轨

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表 3 地面检测法综合特点对比

Table 3. Comparison of comprehensive characteristics of wayside detection methods

检测方法	结构复杂程度	实时监测	成本	检测项点	应用国家
轮轨力检测法	一般	可以	中等	轮轨冲击	中国、美国、德国
超声波检测法	复杂	否	较高	几何缺陷	中国、德国、日本
噪声检测法	简单	可以	较低	振动冲击	中国、日本、美国、澳大利亚
位移检测法	一般	否	中等	几何缺陷	中国、美国
车载振动检测法	简单	可以	较高	振动冲击	中国、希腊、德国、意大利
地面振动检测法	简单	可以	较低	振动冲击	中国、希腊、意大利
图像检测法	简单	可以	较低	几何缺陷	中国、日本、美国、德国
光学检测法	复杂	可以	较高	几何缺陷	美国、德国、爱沙尼亚、捷克
轨道电路中断法	一般	可以	较高	几何缺陷	俄罗斯、瑞典

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