Vibration and Impact Monitoring of Tram Operations

作为基础设施和城市系统主题的一部分，该项目旨在开发一个利用安装在现役列车和有轨电车上的传感器监测铁路轨道状况的系统。铁路轨道在使用过程中，其表面粗糙度不断增大。随着其他形式的劣化，如轨道接缝处的沉降，这导致车辆沿着轨道运行时产生的振动水平增加。振动从轮轨界面的原点开始，通过轨道结构、地面和附近的建筑物传播，然后可能以可感知的振动和/或再辐射噪声的形式引起干扰。英国有相当一部分铁路穿过人口稠密的城市地区。由于靠近住宅物业以及音乐厅和医院等敏感建筑，地面振动是城市铁路运营商的一个主要问题，他们必须不断地进行轨道维护，以将噪音和振动保持在可接受的水平内。目前，这项工作依赖于人工轨道检查，通常每两周进行一次，以便在恶化区域成为重要的振动源之前识别它们。这种检查既耗时又昂贵。显然需要一种更有效的监测方法。本研究旨在开发一种轨道状态监测系统，该系统可以永久安装在运营网络上运行的服务轨道车辆上。它建立在最近的工作基础上，在米德兰地铁的一辆CAF Urbos 3有轨电车上安装了五个轴箱加速度计和相关的数据采集和定位设备。自2016年8月10日以来，该设备一直在米德兰地铁的铁路网上收集有用的数据。在不久的将来，类似的设备可能会安装在伦敦地铁有限公司的一列火车上，为这项研究提供第二个数据来源。主要目标是从垂直轴箱加速度、车辆位置和速度的测量中得出轮轨界面力谱和轨道头粗糙度谱，并将这些谱与车辆在轨道上运行时注入地面的噪音量联系起来。这需要考虑钢轨和轴箱之间的整个机械系统。信号处理的固有挑战包括分离由车轮粗糙度引起的振动，以及对车速进行必要的校正，这两者都可以直接比较来自同一轨道段不同通道的测量结果。下一个目标是创建网络的虚拟状态图。轨道将被分成短段，每段的粗糙度谱将被计算和呈现。每个轨道段的峰值和/或平均水平将被绘制在整个网络的位置上，以使运营商对网络的状况有一个概述。需要进一步研究最合适的段长度，以便在波长分辨率和沿轨道的空间分辨率之间提供有用的折衷。将开发一种网络系统，通过无线通信自动收集车辆的振动数据，并实时更新虚拟状态地图。将开发算法来识别振动瞬态（由未对准的道岔、交叉、不良的轨道接头等引起）以及轨道头粗糙度水平的超标。预计将监测振动水平，以便在振动水平超过一定限度之前安排铁路维修。

This project aims to develop a system for monitoring the condition of railway track using sensors mounted on in-service trains and trams, as part of the infrastructure and urban systems theme. The surface roughness of railway track increases during service. Along with other forms of deterioration, such as settlement at rail joints, this leads to an increase in the level of vibration generated as a vehicle runs along the track. From its origin at the wheel-rail interface, vibration propagates through the track structure, through the ground and into nearby buildings, where it may then cause disturbance as perceptible vibration and/or re-radiated noise. A significant portion of railways in the UK run through densely populated urban areas. The proximity to residential properties, and sensitive buildings such as concert halls and hospitals, means that groundborne vibration is a major concern of urban railway operators, who must work continuously at track maintenance to keep noise and vibration within acceptable levels. Currently, this work relies on manual track inspections, typically undertaken fortnightly, to identify areas of deterioration before they become a significant source of vibration. Such inspections are time consuming and costly. There is a clear need for a more efficient monitoring method.This research aims to develop a track condition monitoring system that can be permanently installed on service rail vehicles running on an operational network. It builds on recent prior work that saw five axlebox accelerometers and associated data acquisition and positioning equipment fitted to one of Midland Metro's CAF Urbos 3 trams. This equipment has been collecting useful data over Midland Metro's rail network since 10th August 2016. Similar equipment may be fitted to one of London Underground Limited's trains in the near future to provide a second source of data for this research. The main objective is to derive the wheel-rail interface force spectrum and the railhead's roughness spectrum from the measurements of vertical axlebox acceleration and vehicle location and speed, and relate these spectra to the amount of groundborne noise injected into the ground as the vehicle runs over the rail. This requires the entire mechanical system between the rail and the axlebox to be considered. Inherent challenges in the signal processing include separating out the vibration caused by wheel roughness, and the necessary correction for vehicle speed, both of which enable measurements from different passes over the same rail section to be compared directly. The next objective is to create a virtual condition map of the network. The track will be divided into short segments over each of which the roughness spectrum is computed and presented. Peak and/or average levels at each track segment will be plotted over location for the entire network to give the operator an overview of the condition of the network. Further research is required into the most appropriate segment lengths that offer a useful compromise between wavelength resolution and spatial resolution along the track. A networked system will be developed that automatically collects vibration data from the vehicles by means of wireless communication, and updates a virtual condition map in real time. Algorithms will be developed to identify vibration transients (caused by misaligned turnouts, crossings, poor rail joints etc.) in addition to exceedances in railhead roughness levels. It is anticipated that the levels of vibration will be monitored in order to schedule rail maintenance before these levels rise above a certain limit.