PhD Studentship: Hybrid Physics-based and Data-driven Modelling for Fault and Anomaly Detection on Wind Energy Systems

This PhD studentship is related to the digitalization of wind energy systems, with the goal of exploring and developing a data-driven paradigm for structural health monitoring and maintenance planning.

The aim of the project is to create a digital twin of the structural and mechanical components of the system, which can be continuously updated to reflect the actual condition of the system and can detect anomalies and faults occurring during operation. As such, it will involve the development of state-of-the-art deep-learning techniques for computationally efficient models, and validation of the developed techniques using real-world data.

Candidates are expected to have a background in structural and/or mechanical engineering, applied mathematics, or physics. Experience in machine learning models and algorithms is desired but not necessary.

For eligible students the studentship will cover home tuition fees plus an annual tax-free stipend of at least £20,112 for 3.5 years full-time. The student would be based in the Department of Engineering in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter, while the possibility of a secondment in RTDT Laboratories in Zürich, Switzerland, will also be offered.

Funding

UK tuition fees and an annual tax-free stipend of at least £20,112 per year

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