Symptomatic damage prediction of the wheelsets and truck components**

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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.**********