PhD Studentship: Human-in-the-loop Machine Learning for Drone-assisted Structural Health Monitoring

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 aim 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.

This is a collaboration between Loughborough University and Toshiba Research Labs in Cambridge.

Supervisors:

Primary supervisor: Prof Nina Dethlefs

Secondary supervisor: Dr Shirin Dora 

Entry requirements:

If you have received or expect to achieve before starting your PhD programme a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Master’s level a degree (or the international equivalents) in computer science, engineering, physics or mathematics and statistics, we would like to hear from you. 

English language requirements:

Applicants must meet the minimum English language requirements. Further details are available on the International website. 

Funding information:

The studentship is for 4 years and provides a tax-free stipend of £20,780 per annum plus tuition fees at the UK rate.

This PhD scholarship is offered by the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience, a partnership between the Universities of Durham, Hull, Loughborough and 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. The project is part of a PhD Research Cluster, Reliability and Health Monitoring Cluster.

How to apply:

To apply for this project, please see the information and follow the procedure on the CDT's website via the 'Apply' button above.

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