drivetrain, comprising the gearbox, generator, and power electronics, remains a major source of failures, accounting for roughly one third of incidents and nearly half of maintenance costs in offshore turbines. Early and accurate fault detection in this subsystem is therefore essential.

Despite widespread deployment, existing CMS solutions are predominantly based on vibration measurements from accelerometers mounted on drivetrain components, and their performance remains limited. These systems often suffer from low fault detection rates, generate false alarms that reduce energy production, or fail to identify faults sufficiently early to avoid costly repairs. Moreover, they are largely ineffective at detecting electrical faults. The lack of commercially available CMS solutions based on electrical measurements is due to inherent complexity of interpreting electrical signatures of mechanical faults in real time. This creates a clear gap and opportunity for more advanced and integrated monitoring approaches.

This project aims to address the identified gap by developing hybrid fault diagnosis methods that combine analytical approaches based on drivetrain physical properties with AI-driven data analysis techniques to enhance the accuracy and effectiveness of fault detection and classification. The work will involve analytical studies, computer simulations, and finite element (FE) analysis, alongside experimental development and validation.

Entry requirements

The minimum entry requirement is 2:1 in Electrical and Electronics Engineering, Mechanical Engineering, Physics.

Mode of study

Full-time or part-time

Start date

1 October 2026

drivetrain, comprising the gearbox, generator, and power electronics, remains a major source of failures, accounting for roughly one third of incidents and nearly half of maintenance costs in offshore turbines. Early and accurate fault detection in this subsystem is therefore essential.

Despite widespread deployment, existing CMS solutions are predominantly based on vibration measurements from accelerometers mounted on drivetrain components, and their performance remains limited. These systems often suffer from low fault detection rates, generate false alarms that reduce energy production, or fail to identify faults sufficiently early to avoid costly repairs. Moreover, they are largely ineffective at detecting electrical faults. The lack of commercially available CMS solutions based on electrical measurements is due to inherent complexity of interpreting electrical signatures of mechanical faults in real time. This creates a clear gap and opportunity for more advanced and integrated monitoring approaches.

This project aims to address the identified gap by developing hybrid fault diagnosis methods that combine analytical approaches based on drivetrain physical properties with AI-driven data analysis techniques to enhance the accuracy and effectiveness of fault detection and classification. The work will involve analytical studies, computer simulations, and finite element (FE) analysis, alongside experimental development and validation.

Entry requirements

The minimum entry requirement is 2:1 in Electrical and Electronics Engineering, Mechanical Engineering, Physics.

Mode of study

Full-time or part-time

Start date

1 October 2026