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2020 | OriginalPaper | Chapter

Reliability Analysis of Rail Fastening System When a Heavy Haul Locomotive Passing Through a Small Radius Curve

Authors: Xuancheng Yuan, Jianzheng Jiang, Shengyang Zhu, Wanming Zhai, Huibin Lou

Published in: Advances in Dynamics of Vehicles on Roads and Tracks

Publisher: Springer International Publishing

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Abstract

In this work, a stochastic dynamics model is proposed to achieve the reliability assessment of the rail fastening system when a heavy haul locomotive passing through a small radius curve. The stochastic dynamics model consists of a locomotive model and a track model, in which the rail fastening system is modeled in detail. Under the assumed extreme condition, the two-point contact wheel/rail forces of the locomotive during passing the curve are obtained through the locomotive model, and then are exported to the track model to compute the screw spike pullout force of the rail fastening system. The vertical and lateral stiffness of the rail fastening system and the friction coefficient between gauge apron bearing and sleeper shoulder are selected as random variables, which are assumed to obey the Gaussian distribution. To perform the reliability analysis, totally 100 combinations of the random variables are generated by the Number Theoretical Method (NTM). The Probability Density Evolution Method (PDEM) is adopted to compute the probability density functions as well as the failure probability of the rail fastening system. The result indicates that the rail fastening systems on the transition curve are generally reliable while some of those on the curve section have a certain risk of the screw spike being pulled out. The rail fastening system located between the transition curve and the curve section is more liable to fail than the others, and the maximum failure probability is about 4.2%.
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Metadata
Title
Reliability Analysis of Rail Fastening System When a Heavy Haul Locomotive Passing Through a Small Radius Curve
Authors
Xuancheng Yuan
Jianzheng Jiang
Shengyang Zhu
Wanming Zhai
Huibin Lou
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-38077-9_39

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