The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey

by Muhammad Nakhaee, Djoerd Hiemstra, Mariƫlle Stoelinga, and Martijn van Noort

Railway systems play a vital role in the world’s economy and movement of goods and people. Rail tracks are one of the most critical components needed for the uninterrupted operation of railway systems. However, environmental conditions or mechanical forces can accelerate the degradation process of rail tracks. Any fault in rail tracks can incur enormous costs or even result in disastrous incidents such as train derailment. Over the past few years, the research community has adopted the use of machine learning (ML) algorithms for diagnosis and prognosis of rail defects in order to help the railway industry to carry out timely responses to failures. In this paper, we review the existing literature on the state-of-the-art machine learning-based approaches used in different rail track maintenance tasks. As one of our main contributions, we also provide a taxonomy to classify the existing literature based on types of methods and types of data. Moreover, we present the shortcomings of current techniques and discuss what research community and rail industry can do to address these issues. Finally, we conclude with a list of recommended directions for future research in the field.

To be presented at the International Conference on Reliability, Safety and Security of Railway Systems: Modeling, Analysis, Verification and Certification (RSSRail 2019) on 4-6 June 2019 in Lille, France.

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PhD position data-driven maintenance optimization

The Data Management and Biometrics group and Formal Methods & Tools groups at the University of Twente seek a PhD candidate for SEQUOIA: Smart maintenance optimization via big data & fault tree analysis, a project funded by the NWO Applied and Engineering Sciences, and the companies ProRail and NS. ProRail is responsible for the Dutch railway network, including its construction, management, maintenance, and safety; NS has the same responsibility for the Dutch train fleed. The project is led by Mariƫlle Stoelinga, Joost-Pieter Katoen and Djoerd Hiemstra.

SEQUOIA aims to improve the reliability of the Dutch railroads by deploying big data analytics to predict and prevent failures. Its scientific core is a novel combination of machine learning, fault tree analysis and stochastic model checking. Key idea is that big data analytics provide the statistics on failures, their correlations, dependencies etc. and fault trees provide the domain knowledge needed to interpret these data. The project outcome aims at developing explainable machine learning techniques that discover causal relations instead of statistical correlations; machine learning of fault trees or of other models that are normally designed top-down by domain experts. The techniques should help ProRail to decrease train disruptions and delays, to lower maintenance cost, and to increase passenger comfort.

The project involves an intense cooperation ProRail and the RWTH Aachen University. The PhD candidate will spend a portion of their time at ProRail. Key project deliverables are efficient analysis algorithms and a workable tool to be used in the ProRail context. For more information, see:
https://www.utwente.nl/en/organization/careers/vacancy/!/phd-position-sequoia/134206

Two PhD positions on Maintenance Optimization for the Dutch railroads

We are hiring two PhD positions on Maintenance Optimization for the Dutch railroads.

The Database and Formal Methods & Tools groups at the University of Twente seek two PhD candidates for SEQUOIA: Smart maintenance optimization via big data & fault tree analysis, a project funded by the Dutch Technology Foundation STW, and the companies ProRail and NS. ProRail is responsible for the Dutch railway network, including its construction, management, maintenance, and safety; NS has the same responsibility for the Dutch train fleed.

Predictive maintenance explained

SEQUOIA aims to improve the reliability of the Dutch railroads by deploying big data analytics to predict and prevent failures. Its scientific core is a novel combination of machine learning, fault tree analysis and stochastic model checking. Key idea is that big data analytics provide the statistics on failures, their correlations, dependencies etc. and fault trees provide the domain knowledge needed to interpret these data. The project outcome aims at fewer train disruptions and delays, lower maintenance cost and more passenger comfort. The project involves an intense cooperation with the RWTH Aachen University and with various engineers from ProRail and NS. The PhD candidates will spend a portion of their time at the ProRail / NS sites in Utrecht.

Apply on-line.