Human-in-the-loop machine learning for drone-assisted Structural Health Monitoring

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience, a partnership between the Durham University, University of Hull, Loughborough University and the University of Sheffield. The successful applicant will undertake six months of training with the rest of the CDT cohort at the University of Hull before continuing their PhD research at Loughborough University.

Inspections of offshore wind turbines, such as identifying damage or ice on turbine blades, anticipating its effects and making decisions on maintenance and repair, as well as estimating remaining useful life (RUL), is an important part of extending the lifetime of a wind turbine as well as the power that can be generated from it. While both tasks are often driven by experts, public data on environmental, meteorological or physical conditions, in combination with satellite and/or climate data, can help make predictions for new, unseen conditions.

The latter is particularly relevant when data is sparse. While public data exists on general environmental conditions and turbine power yield, data around specific combinations of operational and environmental conditions is not always readily available — this is particularly the case for new generations of floating or far offshore turbines, which are much harder to reach and inspect than previous generations much closer to shore, and for which less historical data is available.

This project aims for two key research advances: first, the development of a new human-in-the-loop active learning framework [2, 3], which uses conversational AI to negotiate key decisions related to turbine inspection and maintenance with a human expert [4, 5]. This can be based on a deep reinforcement learning framework, which interactively optimises key performance indicators in the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including UAVs (e.g., drones) that may be used in turbine inspection.

The overall aim is the design of a portable learning system that creates a profile of wear and tear of turbines given the environmental, meteorological and physical conditions they operate under. Such data can inform structural health monitoring for offshore wind turbines or help plan new offshore sites, via estimation of power yield in relation to environmental conditions and logistical constraints, such as closeness to shore, shipping routes, etc.

Application information

All applications should be made online. Under 'Campus', please select 'Loughborough' and select the 'Programme' as 'Computer Science Offshore Wind Energy Sustainability and Resilience'. Please quote the advertised reference number, 'Aura26-ND', in your application.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents. The following selection criteria will be used by academic schools to help them make a decision on your application.

Please note that you may only apply for ONE project offered through the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience.

Please ensure that you familiarise yourself with the Aura CDT website before you apply. The frequently asked questions page and candidate resources page are essential reading prior to applying.

As part of the recruitment process, we ask that you film yourself delivering a presentation, of up to five minutes in length, on 'how your experiences and qualities enable you to contribute to research and innovation for your chosen project'. You will be assessed on the content of your presentation, not your film editing skills, but please be mindful of filming in an appropriate, quiet location.

We also ask that you complete a Supplementary Application form. This includes space for you to provide a link where the shortlisting panel may view your film. The form is available from 'Human-in-the-loop machine learning for drone-assisted Structural Health Monitoring - Aura Centre for Doctoral Training'.

Formal applications should be submitted on the Loughborough University website.

Under 'programme name', select 'Computer Science Offshore Wind Energy Sustainability and Resilience'. Both full-time and part-time modes of study are available. Please quote the advertised reference number in your application - this is 'Aura26-ND' for 'Human-in-the-loop machine learning for drone-assisted Structural Health Monitoring'.

With your application, you need to upload the following supporting evidence:

• Complete transcripts (and final degree certificate[s] where possible). If your qualification documents are not in English, you will need to supply copies of your original language documents as well as their official translation into English
• Your curriculum vitae (CV)
• A completed Supplementary Application form

Uploading the form

When you have completed the Supplementary Application form, please save it as a PDF format and labelled as follows:

Last name_first name PhD application form

Upload the form as part of your application documents through the Loughborough University student application portal. Please do not send your form directly to the Offshore Wind CDT.