A condition-based maintenance methodology for rails in regional railway networks using evolutionary multiobjective optimization

Case study line Braşov to Zărneşti in Romania

Conference paper (2018)

Authors

Alfredo Nunez Railway Engineering -

A. Jamshidi Railway Engineering -

H. Wang Railway Engineering -

J.M. Hendriks Railway Engineering -

I. Ramirez Fonseca Railway Engineering -

J. Moraal Railway Engineering -

R.P.B.J. Dollevoet Railway Engineering -

Z. Li Railway Engineering -

Research Group

Railway Engineering () (TU Delft)

DOI: https://doi.org/10.1109/CEC.2018.8477842

Railway Engineering KPIs Multiobjective optimization Acceleration measurements ARMOEA

To reference this document use:

http://resolver.tudelft.nl/uuid:5000e635-e70a-42f0-bc8a-b5aefc1ef856

More Info

expand_more

Published Date

2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Railway Engineering

Abstract

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using Hilbert-Huang Transform, the locations where the major risks are detected and ssessed with a degradation model. Finally,
evolutionary multiobjective optimization is employed to solve the maintenance decision problem, and to facilitate the visualization of the trade-offs between number of interventions and performance. Real-life measurements from the track from Braşov to Zărneşti in Romania are included to show the methodology.

Files

Nunez_et_al_WCCI2018.pdf

(pdf | 1.22 Mb)

Unknown license

Download not available

08477842.pdf

(pdf | 1.33 Mb)

- Embargo expired in 13-01-2019

Unknown license