Railway rolling stock fleet data analytics towards reliability and performance prediction

Project Details


In this PhD research project, the student will be expected to conduct a comprehensive investigation into the rolling stock fleet in-service, risks, reliability, and operational performance to understand the reasons for the apparent differences in fleet reliability and performance data. A good technical understanding of the mechanical and electrical design configuration of rolling (passenger and freight services) is desirable in this project. Student will also be expected to explore and conduct investigation of the academic literature specifically within the mechanical, statistical, risk, performance, and reliability domain to determine the state-of-the-art dynamic statistical and other modelling methods using the concept of AI and machine learning approach towards anomaly detection, predictive, prescriptive analytics, and maintenance optimisation.

Finally, conduct multiple criteria decision analysis (MCDA) incorporating AI and Machine Learning techniques and use the ANN concept, time series modeling approach linked to dashboard systems.
Short titleTrain fleet performance data analytIcs
Effective start/end date1/02/241/02/27

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 17 - Partnerships for the Goals


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