Title:
Interfacing Data Harnessing, Stochastic Modeling and Optimization for Maintenance Decisions for Railways
Interfacing Data Harnessing, Stochastic Modeling and Optimization for Maintenance Decisions for Railways
Author(s)
De Almeida Costa, Mariana
Advisor(s)
Goldsman, David
Ramos Andrade, António
Ramos Andrade, António
Editor(s)
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Abstract
The increasing demand for cost-effective and transparent solutions for the improvement
of the maintenance decision-making process in railways fuels the development of more
sophisticated and flexible models, which largely exploit the use of data analytics and
optimization tools. At the same time, recent advancements in technologies for railway
condition monitoring and the availability of massive amounts of data allow for more
accurate and reliable fault detection. One obstacle, however, is how to deal with the data
provided by the monitoring equipment as well as the choice of suitable methods to translate
the data into useful information for maintenance scheduling and prioritization. In light of
this, three main stages of the maintenance decision-making process can be identified: i)
data acquisition, ii) modeling approach and, iii) implementation of the policy. Deciding
on which parameter(s) represent the real condition of the asset and accurately measuring
them, guaranteeing appropriate instrument and good measurement precision concerns data
acquisition (step i)). Next, step ii) implies the choice of a comprehensive model that
can tackle all the constraints and uncertainties associated with the deteriorating system,
while providing solutions (in terms of a maintenance policy) in a reasonable amount of
time. Finally, step iii) concerns the ease of implementation of the new maintenance policy,
guaranteeing its practical applicability within the context of the train operating company
under study.
This dissertation aims to provide contributions to these three aspects in terms of
railway track and wheelset maintenance. For both deteriorating systems, the choice of
an appropriate maintenance policy should balance the trade-off between maintenance costs
and costs resulting from the poor-maintained asset, including those arising from potential
safety hazards. This is discussed in the context of the three main stages mentioned above.
The dissertation is structured in five chapters. Chapter 1 provides the introduction,
as well as a brief overview of each of the topics and results presented in the subsequent
chapters. Then, chapters 2 and 3 focus on wheelset maintenance and chapters 4 and 5
focus on railway track maintenance.
In chapter 2, the optimization of railway wheelset maintenance policy is discussed.
This policy is developed based on a data-driven model encompassing estimation of
wear rates and further application of a Markov Decision Process (MDP) approach to
represent possible discretized wheel states, where the problem of maintenance planning
is tackled from the perspective of immediate action cost-optimization. A bidimensional
framework considering discrete intervals of wheel diameter along with a quantitative
variable (kilometers since last turning/renewal) is used to represent the possible wheel
states. In addition, the probability of a defect interfering with the wheel maintenance
schedule is modeled by contemplating survival curves derived from a Cox Proportional-
Hazards model. As a secondary goal, a comparison of the optimized policy with another
wheel’s reprofiling policy that is also ”easy to implement” is provided.
In chapter 3, an investigation around the uncertainty of wheelset inspection data is
made. Previous research has highlighted the relevance of this topic in the decision-making
process surrounding wheelset maintenance actions. In light of this, the investigation aimed
to assess the agreement between data acquired from three different inspection devices,
namely: i) manual (gauge device), ii) a laser device and iii) an under-floor wheel lathe.
Three main wheelset parameters (flange thickness (Ft), flange height (Fh) and flange
slope (qR)) are compared using a Linear Mixed Model (LMM) approach under several real-world
limitations, such as those imposed by serially correlated, unbalanced and unequally
replicated data. Findings supported the use of LMM, showing its ability to capture and
account for the differences among the various groups and highlighting statistical significant
performances of the inspection devices.
In the context of the railway track, chapter 4 presents a spatiotemporal approach for the
modeling and prediction of track geometry faults. Spatial-time data from a train operating
company is considered through a 5-year inspection database. The track twist, defined as the
amount by which the difference in elevation of rails increases or decreases in a given length
of the track, is used as the main track quality parameter. The spatiotemporal approach
considered two Kriging models with a Gaussian correlation function to study a strategic
portion of a track used in heavy-haul transport. A CUSUM (Cumulative Sum) control
chart approach is then applied to identify out-of-control track sections and a Logistic
Regression model is used to get estimates of the probabilities of future out-of-control points
based on the adopted thresholds. Finally, a simple MDP model based on out-of-control
points is proposed to compare different maintenance policies aimed at cost minimization
for different thresholds of twist standard deviation for different track sections grouping
strategies.
Lastly, chapter 5 explores the use of Wavelet Analysis (WA) in the statistical modeling
of railway track irregularities, namely (1) longitudinal level, (2) alignment, (3) crosslevel,
(4) gauge. WA is used to study and reconstruct the four different track geometry
irregularity signals. This investigation aimed at finding wavelets that can appropriately
describe each track irregularity signal studied, and investigating whether the presence of
some high amplitude wavelet coefficients in certain frequencies can be associated with
higher vertical or lateral forces in the wheel-rail contact. The last step is accomplished
by reconstructing the different irregularities signals using wavelets coefficients in various
decomposition levels and studying their impact on Nadal’s safety criterion Y/Q (a critical
quantity for derailment safety assessments) through vehicle dynamics simulations. The
presence of certain wavelets at different decomposition levels allows identifying wavelets
that are more prejudicial in terms of the safety criterion.
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Date Issued
2020-11-03
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Text
Resource Subtype
Dissertation