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Anomaly detection for aircraft health monitoring

  • Full or part time
  • Application Deadline
    Monday, April 01, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Unmanned and autonomous aircraft are being tasked with increasingly complex missions and in greater proximity to other air users and densely populated regions. To ensure the safety of the aircraft and people and property on the ground, aircraft must be imbued with a level of situational awareness equal or greater than that of a trained pilot. As aircraft are operated ever more remotely with longer endurance and larger intervals between human inspections, it becomes increasingly important to monitor the health of the aircraft systems themselves as part of this situational awareness. This project seeks to apply machine learning techniques to the problem of aircraft health monitoring, to detect performance anomalies and to identify categories of faults.

The programme of study will encompass conventional prognostics and health monitoring techniques, feature extraction, classifiers and regression methods, and ultimately more general anomaly detection. The work will be comprised of both lab-based experiments on aircraft and components, and computer-based algorithm development and simulation. It will require a strong engineering or physics background as well as competence in computer science disciplines.

The project will investigate aspects of perception which are pivotal to modern artificial intelligence, in particular in the area of robotics and autonomous systems. Successful application of these techniques will be valuable not only in aircraft applications but in a broad range of autonomous systems. Examples include vehicles such as cars, trains, boats and submarines, machinery on manufacturing and production lines, and, looking to the future, on humanoid robots. In the aerospace field this work will contribute directly to improving the safety of unmanned aircraft operations and expanding the types of application that can be tackled with such vehicles, for example facilitating personal air vehicles or air taxis, and enabling remote civil infrastructure inspection for railways, roads, pipelines and power lines.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

Informal enquiries about the project should be directed to Dr Jonanthan du Bois on email address .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding

How good is research at University of Bath in Aeronautical, Mechanical, Chemical and Manufacturing Engineering?

FTE Category A staff submitted: 61.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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