Influencing factors for condition-based maintenance in railway tracks using knowledge-based approach

Conference paper (2017)

Authors

A. Jamshidi Railway Engineering - CEG
S. Hajizadeh Railway Engineering - CEG
M. Naeimi Railway Engineering - CEG
Alfredo Nunez Railway Engineering - CEG
Z. Li Railway Engineering - CEG
Research Group
Railway Engineering (CEG) (TU Delft)
Copyright: © 2017 A. Jamshidi, S. Hajizadeh, M. Naeimi, Alfredo Nunez, Z. Li
Rail surface defects Bayesian model Condition-based maintenance decision
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Published Date

2017

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Railway Engineering

Abstract

In this paper, we present a condition-based maintenance decision method using
knowledge-based approach for rail surface defects. A railway track may contain a considerable number of surface defects which influence track maintenance decisions. The proposed method is based on two sets of maintenance decision factors i.e. (1) defect detection data and (2) prior knowledge of the track. A defect detection model is proposed to monitor surface defects of the track
including squats. The detection model relies on track images and Axle Box Acceleration (ABA) signals to give both positions of severity and defects. To acquire the prior knowledge, a set of track monitoring data is selected. A fuzzy inference model is proposed relying on the maintenance factors
to give the track health condition in a case study of the Dutch railway network. The proposed condition-based maintenance model enables infrastructure manager to prioritize critical pieces of the track based on the health condition.