Railway wheel tread damage: Detection and consequences of wheel-rail impact loading

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/257333
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Type: Examensarbete för masterexamen
Master Thesis
Title: Railway wheel tread damage: Detection and consequences of wheel-rail impact loading
Authors: Della Valle, Davide
Abstract: Swedish transport authorities are investing large amounts of capital in technologies aimed at detecting railway wheel tread damage to reduce the costs for maintenance and repair of wheelsets and railway infrastructure. Wheel impact load detectors (WILDs) based on load cells and accelerometers is one such type of technology currently in use by Trafikverket (the Swedish Transport Administration). This thesis consists of two related parts. The first part is a statistical analysis of data collected by WILDs to assess accuracy and increase confidence in their performance. The statistical tool used to fit the measured data is a multiple linear regression model. For a few selected wheels with evolving rolling contact fatigue damage, data collected over a one-year period have been analysed. At the early stages of wheel tread degradation, it is shown that the measured dynamic loads are considerably influenced by the train speed. For wheels with severe tread damage, the loads are also significantly influenced by the time since the previous wheel maintenance or replacement (probably related to the increasing wheel tread degradation rate over time). It is observed that the accuracy of the detectors cannot be firmly checked since the data are shown to be influenced by train speed and the time of measurement, and because of lack of measured data within a short time window where the tread damage is close to constant. However, a few observations indicating a need for detector calibration are given by comparing the results from different detectors when based on data registered for the same set of wheels. In the second part of the thesis, a Python script for the Abaqus software has been written to automatically generate a parameterized wheelset model. The Python script allows to easily alter the geometrical features of the wheel design, such as the rim thickness. A non-powered wheelset model is used to evaluate the fatigue resistance of the hollow wheelset axle. A case of warning alarm values registered by a WILD is used to define the periodic loads acting on the rolling contact circle of the wheels. It is shown that the Sines’ criterion is the most suitable to calculate the equivalent stress of the most stressed section. For the given set of applied loads, the calculated stress state in the hollow axle does not induce a fatigue damage to the axle. Key words: wheel impact load detectors, multiple linear regression model, parameterized wheelset model, dynamic analysis.
Keywords: Transport;Teknisk mekanik;Farkostteknik;Transport;Applied Mechanics;Vehicle Engineering
Issue Date: 2019
Publisher: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Chalmers University of Technology / Department of Mechanics and Maritime Sciences
Series/Report no.: Master's thesis - Department of Mechanics and Maritime Sciences : 2019:34
URI: https://hdl.handle.net/20.500.12380/257333
Collection:Examensarbeten för masterexamen // Master Theses

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