PhD Studentship: AI-Driven fault diagnosis for wind turbine generators

Overview

This PhD project at the University of East Anglia, supervised by Dr Abdi Jalebi Salman, develops AI-driven fault diagnosis for wind turbine generators. Reliability is critical to expanding wind power, especially offshore, which already provides around 10 to 15 percent of UK electricity and is central to net-zero targets. Condition monitoring systems support preventive maintenance, but the drivetrain of gearbox, generator and power electronics causes roughly one third of incidents and nearly half of maintenance costs in offshore turbines, so early and accurate fault detection there is essential.

Existing systems mostly rely on vibration measurements from accelerometers and have limited performance, with low detection rates, false alarms that cut energy production, or late detection that misses the chance to avoid costly repairs. They are also largely ineffective for electrical faults, and no commercial systems use electrical measurements because interpreting the electrical signatures of mechanical faults in real time is complex.

The project aims to close this gap with hybrid fault diagnosis methods that combine analytical approaches based on the physical properties of the drivetrain with AI-driven data analysis, improving the accuracy of fault detection and classification. The work includes analytical studies, computer simulation and finite element analysis, alongside experimental development and validation.

Requirements

The minimum entry requirement is a 2:1 in electrical and electronics engineering, mechanical engineering or physics.

What you get

This is a self-funded project open to students worldwide.

Key dates

Study can be full or part time, starting on 1 February 2027. The closing date is 30 November 2026.