Symptomatic damage prediction of the wheelsets and truck components**

在过去的几十年里，铁路运输技术有了长足的进步，出现了高速旅客列车和高轴重货运列车。然而，钢轮和钢轨的相互作用继续带来各种技术挑战。主要挑战之一是如何监控轮轨接口，以可靠地检测其何时受到危害。克服这一挑战对于了解加速磨损机制以及开发有效的基础设施和机车车辆维护系统非常重要。负荷、速度和转向架设计的变化带来了额外的复杂性。因此，预测车辆管理系统的发展至关重要，该系统还应能够将数据转化为有意义的信息，以便及时进行维修干预，以减少维修费用、意外服务中断和潜在的脱轨风险。这项研究旨在开发一种基于路旁测量系统的预测性维护方法，该系统由总部位于魁北克省里高德的Wayside检验设备公司(WID)构思。WID的基于光学的转向架性能检测器(BPD)系统称为TBOGI，通过测量每个轮对的横向偏差和攻角来评估转向架状况，以预测系统稳定性。由于轮对偏差可能是由机车车辆部件中的一系列缺陷共同作用造成的，因此与轮对磨损没有直接关系。该项目旨在对TBOGI轮对磨损预测系统进行分析，包括在不同运行条件和阈值下的验证。该项目将涉及：组织和分析与选定铁轨的轨道、车队和运行状况有关的数据(将由世界开发署获得)；开发系统的虚拟模型并进行验证；开发用于预测磨损率的模型；使用一系列可能的缺陷和次优条件下的磨损预测进行模型模拟；以及TBOGI系统的测量值与模拟预测的磨损值之间的相关性。

Railway transportation technologies have advanced significantly over the past few decades leading to high-speed passenger trains and high axle-load freight trains. The interactions of the steel wheel and the rail, however, continue to pose various technical challenges. One of the main challenges has been how to monitor the wheel/rail interface to reliably detect when it is compromised. Overcoming this challenge is important for understanding accelerated wear mechanisms, and develop an effective infrastructure and rolling stock maintenance system. Variations in load, speed, and bogie design introduce additional complexities. The developments in predictive rolling stock management systems is thus vital, which should also be able to transform the data into meaningful information for timely maintenance interventions in order to reduce the maintenance costs, unscheduled service interruptions and potential derailment risks. This study is aimed at development of a predictive maintenance approach based on a wayside measurement system, conceived by Wayside Inspection Devices Inc. (WID), headquartered in Rigaud, Québec. WID's optical-based BPD (Bogie Performance Detector) system, called TBOGI, assesses bogie condition by measuring each wheelset's lateral deviation and angle-of-attack for predicting system stability. Since the wheelset deviations may be caused by the coupled effects of an array of defects in the rolling stock components, it cannot directly relate to wheelset wear. The proposed project is aimed at analyses of the TBOGI system for predicting wheelset wear, including verification under different operating conditions and threshold values. The project will involve: the organization and analyses of data (to be acquired by WID) related to track, fleet and operating conditions of a selected railway track; the development of a virtual model of the system and its validation; the development of a model for predicting the wear rate; model simulations using a range of possible defects and prediction of wear under sub-optimal conditions; and correlations between the measured values from the TBOGI system with the wear predicted from the simulations.**********