Studentships are available to UK and eligible EU citizens with (or about to obtain) a minimum of a 2.1 Masters or 1st Class Bachelor’s degree in Engineering, Physics or Maths. Experience in the following areas is beneficial but not necessary:
- Wind Energy
- Signal Processing
- Data Analytics
- Machine Learning
A new Centre for Doctoral Training (CDT) at the University of Strathclyde will train researchers to EngD and PhD level in wind and marine energy. Seventy PhD students will be recruited for four years of training and research.
In collaboration with the CDT, Natural Power are co-funding this EngD studentship. Natural Power is a leading independent renewable energy consultancy and services provider. The research student hired on the CDT/Natural Power studentship will enjoy a comprehensive training programme and an accredited IET/IMechE scheme leading to CEng status.
Our CDT offers a unique programme, combining training and research that will aid graduates in transitioning into careers in the wind and marine energy sectors. Training covers all aspects of wind and marine renewable energy systems including the wider socio-economic context. Parallel to the training outlined above the student will be carrying out research in the area of wind turbine drivetrain remaining useful life prediction as outlined below.
In order to optimally make decisions for wind turbine maintenance, predictions on the future health states of the wind turbine drivetrain must be carried out. Prognostics is the process whereby past and present condition monitoring data of a system or component is used to project its health state into the future. The wind turbine drivetrain is a critical subassembly in terms of downtime and replacement costs, therefore, it is very important to monitor it and perform accurate prognositcs. Monitoring is usually being done using vibration, SCADA, and oil data. An integrated decision support system using data fusion can increase the maintenance action confidence.
This EngD will focus on the wind turbine drivetrain fault detection, isolation and remaining useful life estimation using advanced time-frequency methods and taking into account component dependencies. The work will involve the following steps:
- Research of various time-frequency signal processing methods for wind turbine vibration signals, such as wavelets.
- Extract health indicators.
- Model dependencies between components.
- Use data fusion techniques to combine various data streams.
- Develop a multi-component degradation model
- Model lifetime extension schemes.
The work will be validated using vibration data from operating wind farms.
The project is co-funded by Natural Power and EPSRC. The funding includes full tuition fees, along with a generous stipend and support with travel costs for the duration of the project.
The supervision team for this project is Dr James Carroll, Dr Sofia Koukoura and Dr Alasdair McDonald. Their expertise is in wind turbine drivetrains, reliability and machine learning.
For further details on our Centre, please click here.
For further enquiries related to the Centre for Doctoral Training contact: Drew Smith, CDT Administrator,
Tel: 0141 548 2880, Email: email@example.com
For further enquiries related to the EngD research topic contact:
firstname.lastname@example.org or email@example.com
How to apply
To apply, please follow the application link below. The closing date for applications is 21 February 2020.