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University of Southampton

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PhD in Early Wear Detection for Prescriptive Maintenance (RxM) of Machines University of Southampton in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Competition funded PhD project.

Deadline

Jan 29, 2027

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Country

United Kingdom

University

University of Southampton

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Keywords

Computer Science
Mechanical Engineering
Electrical Engineering
Materials Science
Energy Engineering
Tribology
Sensor Fusion
ML

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About this position

PhD opportunity at the University of Southampton in Early wear detection for prescriptive maintenance (RxM) of machines.

This postgraduate research project is focused on developing next-generation technologies for the early detection of wear and damage in wind turbine journal bearings. The research combines tribology, instrumentation, machine learning, and renewable energy to identify the earliest signatures of surface degradation before catastrophic failure occurs.

You will investigate multimodal sensing approaches, including electrostatic charge sensing arrays, vibration, acoustic emission, temperature, and lubricant condition monitoring. The project also involves advanced sensing hardware, data fusion algorithms, and physics-informed AI models to transform raw sensor signals into real-time assessments of bearing health and remaining useful life.

The project is run within the Faculty of Engineering and Physical Sciences and is in collaboration with the University spin-out TriboAI Ltd and industrial partners. This gives the successful candidate the chance to work at the interface of academic research and industry on technologies that improve the reliability, availability, and sustainability of future wind energy systems.

Degree level: Doctor of Philosophy (PhD). Funding: competition funded. Entry requirement: at least a 2:1 honours degree. Deadline: 29 January 2027.

Ideal applicants will have an interest in sensing, signal processing, AI/data fusion, condition monitoring, tribology, and mechanical systems. The project is especially relevant for students aiming to work on predictive maintenance, smart manufacturing, and renewable-energy reliability.

Funding details

Competition funded PhD project.

What's required

Applicants should have at least a 2:1 honours degree. The project is suitable for candidates interested in tribology, instrumentation, machine learning, renewable energy, sensing hardware, data fusion, and physics-informed AI.

How to apply

Apply through the University of Southampton postgraduate research project application process. Review the full entry requirements and funding information on the project page before submitting your application.

More information can be found here

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