Symptomatic damage prediction of the wheelsets and truck components**

轮对和卡车部件的症状损坏预测**

• 批准号: 533820-2018
• 负责人: Rakheja, Subhash
• 金额: $ 1.79万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=662940
• 关键词: Symptomatic; damage; prediction; wheelsets; truck

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.**********

在过去的几十年里，铁路运输技术取得了显着进步，导致高速客运列车和高轴载货运列车。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车轮/轨道接口，以可靠地检测何时受到损害。克服这一挑战对于理解加速磨损机制，开发有效的基础设施和机车车辆维护系统非常重要。载荷、速度和转向架设计的变化带来了额外的复杂性。因此，预测性车辆管理系统的开发至关重要，它还应该能够将数据转换为有意义的信息，以便及时进行维护干预，从而降低维护成本，计划外服务中断和潜在的脱轨风险。这项研究的目的是在路边测量系统的基础上，设想路边检测设备公司的预测性维护方法的发展。(WID)总部位于魁北克省Rigaud。WID的基于光学的BPD（转向架性能检测器）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的耦合效应引起的，因此它不能直接与轮对磨损有关。该项目旨在分析TBOGI系统预测轮对磨损，包括在不同的操作条件和阈值下的验证。该项目将涉及：数据的组织和分析（将由WID获得）与选定铁路轨道的轨道、车队和运营条件有关的信息;开发系统的虚拟模型并对其进行验证;开发预测磨损率的模型;使用一系列可能的缺陷进行模型模拟并预测次优条件下的磨损;以及TBOGI系统测量值与模拟预测磨损之间的相关性。*