Thesis-2010-Faiz.pdf (856.08 kB)
An empirical rail track degradation model based on predictive analysis of rail profile and track geometry
thesis
posted on 2010-07-06, 08:52 authored by Rizwan Bin FaizIt is generally observed that the condition of rail tracks degrades rapidly over time
until and unless effective maintenance is carried out. In the rail industry, rail
maintenance actions are usually reactive, which means that maintenance is carried
out after a defect has been identified. Unfortunately, this approach can lead to
general safety concerns and may result in costly maintenance. Predictive
maintenance, which aims to predict the future behaviour of track degradation based
on the analysis of already recorded data, can be used to identify defects in advance,
thus providing a solution for the above safety and cost concerns.
Two important questions for which answers are sought in predictive maintenance
of rail track are: where does the fault occur and when. The aim of the research
presented in this thesis is to develop a novel predictive rail track degradation model
that answers the above questions. The proposed model consists of an alignment
component for effective alignment of data and a degradation component for
understanding rail track degradation based on rail profile and track geometry
parametric analysis.
The thesis takes an incremental approach to data alignment proposing three
different algorithms namely, distance alignment, fixed window based alignment
and parameter based alignment. It is proven that the latter approach provides the
most accurate data alignment algorithm.
The degradation component of the proposed model is based on a comprehensive
multivariate and univariate analysis. In multivariate analysis, parameters of a base
file i.e. a file consisting of parameters belonging to the same segment of the rail
track at a given time of measurement are predicted using all other parameters of the
same file. In univariate analysis, every parameter of a given base file is predicted,
temporally, from the corresponding parameters in the previous base files. Such
contribution analysis manifests the level to which each parameter contributes in
predicting other parameters and over time. Subsequent to univariate and
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multivariate analysis the predictive errors are thresholded into either exceedences
i.e. they exceed the threshold line, needing immediate maintenance, or normal i.e.
they are below the threshold line, needing no immediate maintenance.
The research presented in this thesis shows that in multivariate analysis, rail profile
parameters were predicted with 97% prediction accuracy below threshold, whereas
track geometry parameters were predicted with 99% prediction accuracy below
threshold. Both univariate and multivariate analysis will serve as the basis in
monitoring track conditions and thus finding track degradation problems. This will
greatly aid in planning predictive track degradation by providing an objective
means of evaluating track conditions and hence the over all life of the rail track will
increase.
History
School
- Science
Department
- Computer Science
Publisher
© Rizwan Bin FaizPublication date
2010Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.Language
- en