EngD in the new EPSRC Centre for Doctoral Training in Wind and Marine Energy Systems and Structures at the University of Strathclyde collaborating with Natural Power.
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. Funded by the Engineering and Physical Sciences Research Council (EPSRC), a total of 70 PhD students will be recruited for four years of training and research.
In collaboration with the CDT, Natural Power are co-funding an EngD studentship. Natural Power is a leading independent renewable energy consultancy and services provider. The research student hired on the CDT/Natural Power collaborative research project 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. As part of the CDT, the student will join a cohort of 15 students who will undertake the same training programme as well as a wider family of over 150 existing students and alumni. You will be supported by the staff and students of the CDT, as well as a dedicated academic supervision team. 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.
Research project overview:
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 prognostics. Monitoring is usually 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:
The work will be validated using vibration data from operating wind farms.
As a collaborative research project, the research student will work together with Natural Power and the University of Strathclyde research teams, spending time in both organisations.
The CDT values diversity and welcome applications from all sections of the community. As part of an initiative to encourage women into research, funding is available from the CDT to cover early years’ childcare in the University’s on campus nursery.
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 a numerical degree such as Engineering, Physics or Maths. Experience in the following areas is beneficial but not necessary:
- Wind Energy
- Signal Processing
- Data Analytics
- Machine Learning
To apply, please follow the application link below. The closing date for applications is 21/02/2020
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: firstname.lastname@example.orgContinue reading
|Title||EngD: Remaining useful life and lifetime extension of wind turbine drivetrains|
|Employer||University of Strathclyde|
|Job location||16 Richmond Street, G1 1XQ Glasgow|
|Published||January 13, 2020|
|Application deadline||February 21, 2020|
|Job types||Engineer,   PhD  |
|Fields||Energy Technology,   Data Mining,   Data Structures,   Programming Languages,   Mechanical Engineering,   Machine Learning,   Signal Processing,   Renewable Energy  |