Autonomous Artificial Intelligence Enabled Drones to Inform Predictive Maintenance Actions in the Railways and Roads
Eligibility: UK/EU/International graduates with the required entry requirements
Funding details: This is a full studentship, which includes tuition fees and living expenses for a doctoral candidate over 3.5 years. Stipend rates set by UKRI with an annual projected average increase of 1.25% per year. Stipend for the first year will be £15,009.
Duration: Full-time – between three and three and a half years fixed term
Application deadline: 30th April 2020
Interview dates: Estimated date is 18th May 2020 - date will be confirmed to shortlisted candidates
Start date: September 2020
For queries contact: Dr Mauro S. Innocente
Predictive maintenance in the railways and roads is paramount to maintain these critical systems in continuous operation. Maintenance issues include missing fasteners, deformed track geometry, subgrade/ballast instabilities, structural health problems, failures, obstructions, potholes, roadside vegetation, damaged guardrails, etc. Inspections typically involve foot-patrols, trolleys and/or measuring vehicles. These can be accurate at the expense of significant manpower and time. Maintenance is often reactive (too late) or preventive, regularly performed to decrease the probability of failure (too early). Instead, Predictive Maintenance (PdM) is informed by the infrastructure condition rather than its expected lifespan. PdM has been attempted using pattern recognition systems based on test-vehicle data. However, this requires the interruption of the normal traffic.
The use of remotely-controlled monitoring drones to identify maintenance needs has been proposed, with preliminary trials showing that data-acquisition time is drastically reduced. Drone-based inspection does not require stopping the traffic and can work in areas inaccessible to human operators. However, the use of Artificial Intelligence (AI) enabled autonomous drones is yet to be explored.
This project will investigate the railways and roads maintenance current practices and technologies, as well as relevant state-of-the-art autonomous navigation and pattern recognition algorithms. The aim is to identify application-based improvements and to develop a drone-based autonomous intelligent system to detect maintenance needs without disrupting the normal traffic of these transport systems.
This is a full studentship, which includes tuition fees and living expenses for a doctoral candidate over 3.5 years.
Stipend rates set by UKRI with an annual projected average increase of 1.25% per year. Stipend for the first year will be £15,009.
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- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
- The potential to engage in innovative research and to complete the PhD within 3.5 years.
- A minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).
- A first degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Robotics, Artificial Intelligence (AI), or another relevant discipline.
- A postgraduate degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Robotics, AI, Autonomous Systems, Data Science, or another relevant discipline.
- Experience of working with ROS.
- Experience of working with Gazebo simulator.
- Expertise in Pattern Recognition.
- Expertise in Guidance, Navigation and Control.
- Programming skills in Matlab, Python and/or C++.
- Prior knowledge of road and/or railways maintenance.
How to apply
To find out more about the project, please contact: Dr Mauro S. Innocente.
All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.Apply to Coventry University