钢轨波磨检测原理、方法及装置研究进展

doi:10.3901/JME.2025.10.191

摘要/Abstract

摘要： 对钢轨波磨的检测原理进行系统梳理，总结其优缺点与适用范围，阐述依据不同检测原理的检测方法，综述常见的波磨静态和动态检测装置及新一代检测、监测装置的研发进展，并展望波磨检测的研究方向。研究结果表明：钢轨波磨检测原理可分为弦测法、惯性基准法、信号重构法、机器视觉法与时序建模法五类。弦测法具有传递函数不为1的固有缺陷，波长越短幅值振荡越剧烈，可使用组合弦模型测量波磨。惯性基准法依据惯性原理，传感器可安装在轴箱、构架及车体等多个位置，低速下惯性传感器的响应越来越小，噪声与趋势项成分逐渐占据主导。信号重构法利用数字信号处理技术分解轴箱加速度、构架加速度、轮轨噪声及车内噪声信号等多源数据，从中提取关于波磨的有效信息。机器视觉法基于图像处理和模式识别技术，使计算机能够感知、理解和解释钢轨图像中的内容，主要包括基于图像处理、基于激光摄像和基于三维点云重构三种方法测量波磨。时序建模法将波磨识别转换为分类或回归问题，通过机器学习、深度学习技术建立波磨与振动、噪声等响应的映射关系，从而实现对波磨的检测。钢轨波磨的检测、监测装置朝着智能化、集成化和小型化的便携测量方向发展。

关键词: 钢轨波磨, 检测原理, 检测装置, 机器视觉, 深度学习

Abstract: A systematic review is conducted on the detection principles of rail corrugation, with advantages, limitations, and applicable scope summarized. Detection methods based on different principles are elucidated, and an overview of common static and dynamic detection devices for corrugation is provided, along with research progress on next-generation detection and monitoring devices. Research prospects for corrugation detection are discussed. Detection principles for rail corrugation are categorized into five types: chord measurement method, inertial reference method, signal reconstruction method, machine vision method, and time-series modeling method. Inherent drawbacks, such as non-unit transfer function and severe amplitude oscillation for shorter wavelengths, are associated with the chord measurement method. Corrugation can be measured effectively using a combination of chord models. The inertial reference method, based on inertia, can be installed at multiple positions, such as axle boxes, frames, and the car body. At low speeds, inertial sensor responses diminish, while noise and trend components gradually dominate. Digital signal processing techniques are used by the signal reconstruction method to decompose data from sources like axle box acceleration, frame acceleration, wheel-rail noise, and in-car noise, extracting valuable information about corrugation. Rail images are perceived, understood, and interpreted by computers using the machine vision method, which is based on image processing and pattern recognition technologies. This method mainly comprises three approaches: image processing-based, laser camera-based, and 3D point cloud reconstruction-based methods for corrugation measurement. Corrugation recognition is transformed into classification or regression problems by the time-series modeling method. Through machine learning and deep learning techniques, mapping relationships between corrugation and responses like vibration and noise are established, achieving corrugation detection. Detection and monitoring devices for rail corrugation are advancing towards intelligent, integrated, and portable measurement solutions.

Key words: rail corrugation, detection principle, detection device, machine vision, deep learning

中图分类号:

U213

肖宏, 王阳, 刘秀波, 崔旭浩, 张智海, 金锋. 钢轨波磨检测原理、方法及装置研究进展[J]. 机械工程学报, 2025, 61(10): 191-214.

XIAO Hong, WANG Yang, LIU Xiubo, CUI Xuhao, ZHANG Zhihai, JIN Feng. Review on Detection Principle, Method, and Device of Rail Corrugation[J]. Journal of Mechanical Engineering, 2025, 61(10): 191-214.

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