Project Ref: NGCM-0405
Supervisor: Prof AJ Keane
Research Group: Chemistry: Computational Systems Chemistry
Co-supervisor: Dr. O. Hovorka
Research Area: Computational Engineering
Making effective railway maintenance plans requires a near real-time understanding of the health of assets. When assets degrade this increases the risk to network availability and safety.
Over recent years there has been a surge in the use of built-in, train-borne and air-borne sensors capturing data and imagery. These sources provide different views of the current state of an asset. The availability of such diverse data provides an exciting opportunity to explore how cross-fertilisation of sources could drive the next generation of intelligent maintenance systems for the rail industry.
The track system that includes earthworks, drainage, S&C and the vegetation that surrounds this is a key driver: Track maintenance cost alone is 50% of the total cost of maintenance. In the last 2 years, 50% of asset-failure delay costs were attributable to track. Consequently how the track is managed is both a key driver of cost and network disruption. This project focusses on the exploitation of sensor data relevant to the track system.
The goal would be a demonstrator that exploits a combination of data monitored at different frequencies processed using the latest generation of data-mining and deep learning methods based on powerful GPU systems. Data sources include:
* Track geometry monitored several times per day from in-service trains where available.
* Environmental data monitored several times per day from external systems
* Track geometry and rolling contact fatigue measured periodically by NR fleet
* Condition of earthworks, drainage and vegetation based on infrequent surveys/lidar imagery.
* Historic delay and failure incidents.
* Network traffic/infrastructure utilisation
Monitored data is now available to measure the actual condition and rate of change of condition as a consequence of actual track use. Advanced analytical techniques can be applied to the available datasets to search for predictive models of how condition will change with use.
The project would measure the benefits in moving from a static plan for scheduled maintenance to a dynamic risk-based plan that is continually refreshed based on measured and predictively modelled changes in the track system.
This project will be undertaken in conjunction with our Centre for Doctoral Training in Next Generation Computational Modelling and Network Rail, who will provide for an extremely attractive and fully competitive tax-free stipend.
Candidates for this exciting role would:
* Demonstrate an enquiring mind with a relentless drive to seek new insights from data
* Demonstrate knowledge of statistics and computational techniques
* Want to develop skills in data science
* Want to develop skills in information technology to effectively exploit big data
* Want to contribute to the Digital Railway vision.
If you wish to discuss any details of the project informally, please contact Prof Andy Keane, CED research group, Email: firstname.lastname@example.org, Tel: +44 (0) 2380 59 2944.
Keywords: Civil Engineering, Computer Science, Operational Research, Software Engineering
Support: All studentships provide access to our unique facilities and training and research support .