Back to Search Start Over

ALIGNING TRACK GEOMETRY REQUIREMENTS FOR ROLLING STOCK QUALIFICATION AND TRACK MANAGEMENT.

Authors :
Nadarajah, Nithurshan
Barnes, Jonathan
Brown, David Venn
McLeod, John
Dingyang Zheng
Source :
EA National Conference Publications; 2023, p883-896, 14p
Publication Year :
2023

Abstract

Qualification of a rolling stock design involves a number of tests that ensure safe and acceptable performance under a range of conditions. However, the test conditions prescribed in the standards may not necessarily reflect the condition of the network as there is often misalignment between the rolling stock and track maintenance standards. Aligning the requirements of vehicle qualification test conditions to the track maintenance standards is critical to ensure that the performance of the rolling stock on the network is acceptable. This challenge is pertinent for rolling stock qualified as per European/UIC standards. For example, UIC 518 provides guidelines for the assessment of rolling stock performance in relation to safety, track fatigue and ride quality. It defines test conditions as a combination of inertial vertical and lateral faults. However, track geometry, particularly in Australia, is typically managed with respect to chordal fault amplitude or a track condition index (TCI), which is a statistical combination of various geometry parameters. This paper explores semi-analytical means to align the requirements of rolling stock qualification conditions to the track maintenance requirement. To the best of the authors' knowledge the methodology described in this paper is a first of its' kind. The paper also described the limitations of the proposed methodology and some pragmatic considerations in implementing the technique described in this paper. An application of this method is demonstrated using track geometry data from the Sydney Trains network. [ABSTRACT FROM AUTHOR]

Details

Language :
English
Database :
Complementary Index
Journal :
EA National Conference Publications
Publication Type :
Conference
Accession number :
178271690