A Novel Description Method for Track Irregularity Evolution

DOI

10.2991/ijcis.2011.4.6.29How to use a DOI?

Keywords

Railway; Track Irregularity; Inspection Data; Fitting Mode; Machine Learning Model.

Abstract

Track Irregularity has a significant influence on the safety of train operation. Due to the fact that the extremely large number of factors affect track irregularity it is challenging to find a concise yet effective mathematical method to describe the evolution of track irregularity. In this paper, inspection data generated by GJ-4 track inspection cars from Jinan Railway Bureau in China were analyzed to identify the characteristics of track irregularity changes common to different mileage points. Based on these characteristics, a multi-stage linear fitting model to describe the pattern of track irregularity evolution over time was developed. The availability of new inspection data will make the model revise itself. In this sense, the model is a machine learning model. Finally, inspection data from the Beijing-Shanghai Railway Line (Jing-Hu Line) were used to verify the model.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Peng Xu
AU  - Rengkui Liu
AU  - Futian Wang
AU  - Quanxin Sun
AU  - Hualiang Teng
PY  - 2011
DA  - 2011/12/01
TI  - A Novel Description Method for Track Irregularity Evolution
JO  - International Journal of Computational Intelligence Systems
SP  - 1358
EP  - 1366
VL  - 4
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.6.29
DO  - 10.2991/ijcis.2011.4.6.29
ID  - Xu2011
ER  -