35.7t轴重重载铁路轨道关键设计参数研究

我国面临着发展重载铁路和承担海外重载铁路的设计任务,美国、加拿大、澳大利亚等国重载铁路的轴重普遍达到35.7 t,结合海外项目,以35.7 t轴重货车为例,对此轴重条件下的轨道结构主要设计参数进行研究。轴重的提高对重载铁路轨道部件提出更高的要求。采用商业有限元软件ANSYS,建立轨道-路基系统有限元模型,主要研究钢轨类型、轨下垫板刚度、道床状态、路基基床参数对35.7 t轴重货车的轨道结构静力学特性的影响,为轴重35.7 t轨道结构的关键参数选取提供建议。     

重载铁路横向力对钢轨倾覆影响研究

研究目的:随着我国重载铁路的不断发展,轮轨之间的横向力在逐渐加大,轨道横向抗力已成为控制线路稳定性及行车安全的关键因素。因此,研究相应的轨道临界横向力,已成为铁路运输安全的重要保证。研究结论:本文通过ANSYS有限元软件建立结构模型,以重载铁路为例,分析了横向力的大小对扣件受力、钢轨轨头横向变形、钢轨轨底抬高量、轨下垫板变形的影响,得出轨道各部件的受力变形规律。针对模拟的轨道结构,钢轨受到横向力大于250 kN时,可能出现倾覆的危险。     

美国凤凰城都会区新型轻轨车辆

为解决美国凤凰域都会区公交系统不够完备的问题,首次建设一条轻轨线路并研发适应凤凰城都会区特殊气候条件的新型轻轨车辆.在安全性、可视化、人性化、人机工程等方面进行创新设计,使这种车辆成为当今世界上最先进的轻轨车辆之一.     

钢轨焊前断面廓形参数检测方法研究

钢轨断面的廓形参数对钢轨焊接后的平直度、焊后打磨、钢轨品质等影响重大。针对钢轨焊接前的轨端廓形检测,在激光传感器采集的廓形数据基础上,详细论述数据的处理方法和过程,主要包括数据的预处理、数据的融合、分段拟合、廓形的计算等,分析不同方法针对该研究目的的可行性、适应性以及准确性,最终选择移动平均法进行数据的预处理,采用矩形标准块和圆柱标准块标定进行数据融合,基于协方差矩阵的特征值选取分段点,最小二乘法进行分段拟合。试验数据表明,该方法对数据处理的效果良好,可以高效精确地检测出钢轨焊前断面的廓形尺寸。     

基于小波包能量熵的钢轨波磨检测方法

针对目前轨道波浪型磨损检测效率慢、成功率低等问题,提出一种基于小波包能量熵的轨道波磨检测技术。首先建立轨道波磨简化模型及车辆集总化参数模型并搭建SIMPACK仿真动力学模型,其次创新性地将小波包和能量熵相结合应用在轨道波磨检测领域,再通过仿真模型得到不同波磨分组下的轴箱振动响应,然后对振动加速度信号进行4层小波包分解并计算各节点的小波包能量和小波包能量熵,最后通过对比分析仿真结果确定轨道波磨故障类型。     

隧道内支承块空吊对钢轨竖向位移影响

为研究隧道内列车荷载下弹性支承块空吊对钢轨竖向位移的影响,基于银洞坡和定水坝隧道现场实测数据,建立考虑支承块空吊的力学模型,分析连续1~3块支承块空吊情况下钢轨竖向位移的超限情况。实测和研究结果表明:隧道内实测的空吊位置和量值具有不确定性特征,空吊现象引起的钢轨竖向位移有超过安全指标的情况;单块支承块空吊值在10 mm以下,钢轨竖向位移不出现超限情况,当连续2块或连续3块支承块空吊量大于3 mm时,钢轨竖向位移出现超限情况,对于隧道内复杂的支承块空吊病害,建议控制空吊量在3 mm以内,同时应尽量避免连续空吊情况。     

基于机器视觉的钢轨表面检测光学模型的研究

在机器视觉的检测中,图像是整个系统中最重要的原始数据,其质量决定了后期图像处理的效果和速度。为探讨高质量图像的采集,提出一种基于线阵CCD相机和线阵光源的钢轨表面缺陷检测的光学理论模型,分析线阵CCD采集系统中振动模糊的原因,推导出图像灰度值与系统振动幅度和缺陷深度的关系,研究光源照射角度和相机拍摄角度对图像灰度和缺陷区域对比度的影响,并通过实验验证模型的合理性。结果表明:缺陷区域图像灰度值随着钢轨表面缺陷深度增大而降低,采用较低的光源照射角度可增大缺陷与背景的对比度,突出缺陷特征便于后期图像处理的缺陷识别。     

城际轨道交通信号系统设计方案探讨

针对城际轨道交通信号系统工程,讨论了信号系统适用的几种方案。考虑到工程实际条件,从运营需求、系统功能、实施难度等方面对几种信号系统方案进行了比选讨论,对城际轨道交通信号系统的建设具有一定的参考意义。     

Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

Rail travel and the Leisure market in great Britain

The purpose of this paper is to examine the factors influencing and inducing leisure travel; to convey the sensitivity of rail travel receipts to the performance of the economy and more particularly family and personal disposable incomes; to examine the potential for further market research; and finally, to determine ways and means of attracting marginal revenue in the sustainable recreation/tourism sphere to a major public sector asset.

The paper focuses on leisure marketing by Britain's national railway corporation, British Rail (BR), and the relevant activities of its three passenger business sectors: Network SouthEast (NSE), which operates regional and local services in London and the South East (though not the London ‘tube’ system); Regional Railways, which operates regional and local services elsewhere in Great Britain; and InterCity, which operates the long distance express services.

The paper reviews BR's fares promotions of recent years and in particular draws on some examples taken from Regional Railways. This sector is now the only business of British Rail which in principle is subject to a significant public grant, to meet the obligation of a minimum level of service on social grounds. Of the other two passenger businesses, Network SouthEast receives a grant but the current intention is that it should operate on a breakeven basis from the 1992/93 financial year; InterCity runs on a purely commercial basis.