AI-Driven Condition Monitoring System for Offshore Wind Generators (ABDIJALEBIS_U27SCI)
Key details
- Application deadline
- 24 February 2027 (23:59 UK Time)
- Location
- UEA
- Funding type
- Competition funded project (Home students only)
- Start date
- 1 October 2027
- Mode of study
- Full-time
- Programme type
- PhD
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Project description
Primary Supervisor - Dr Salman Abdi Jalebi
Reliability is essential to the continued expansion of wind power, particularly offshore, which contributes around 10–15% of UK electricity generation and plays a key role in achieving net-zero targets. Condition monitoring systems (CMSs) improve turbine reliability and availability by enabling preventive maintenance, reducing operational costs and the levelised cost of energy. However, drivetrain components, including gearboxes, generators and power electronics, remain major sources of failure, accounting for approximately one-third of incidents and nearly half of offshore turbine maintenance costs.
Existing CMS solutions predominantly rely on vibration measurements using accelerometers mounted on drivetrain components. Their effectiveness is limited by missed or delayed fault detection, false alarms and poor sensitivity to electrical faults. Despite their potential, electrical measurement-based CMS solutions remain commercially underdeveloped, largely due to the complexity of interpreting electrical signatures associated with mechanical and electrical faults in real time.
This project aims to address these limitations by developing advanced hybrid fault diagnosis methods that integrate physics-based analytical models of drivetrain behaviour with AI-driven data analysis techniques. The objective is to improve fault detection accuracy, early fault identification and fault classification while reducing false alarms.
The research will combine analytical investigations, numerical simulations, finite element (FE) modelling and experimental development and validation. The anticipated outcome is a more reliable and effective condition monitoring approach for offshore wind generators, supporting predictive maintenance, reducing downtime and maintenance costs, and improving the long-term operational performance of offshore wind energy systems.
Entry requirements
The standard minimum entry requirement is 2:1 in Electrical/Electronics Engineering Computer Science.
Funding
This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is £21,805 and will be subject to any agreed inflationary uplift for 2027/28.