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Advanced data-driven prognostics for improved operation and maintenance recommendations


   Department of Electronic and Electrical Engineering


About the Project

EngD in the new EPSRC Centre for Doctoral Training in Wind and Marine Energy Systems and Structures at the University of Strathclyde collaborating with Renewable Energy Systems (RES).

Developing Future Leaders in Wind and Marine Renewable Energy:

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). In collaboration with the CDT, RES are co-funding an EngD studentship. RES is the world’s largest independent renewable energy company. The research student hired on the CDT/RES studentship will enjoy a comprehensive training programme which is accredited by both IET and IMechE, providing a pathway for development to achieve CEng status.

Our CDT offers a unique programme, combining training and research that will aid graduates 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 condition monitoring as detailed in the research project overview. Throughout the 4-year project the researcher will spend time at both the University of Strathclyde and within RES’s Analysis and Optimisation (AO) team, offering the opportunity to acquire skills in research, working towards a doctorate degree while also gaining experience in wind farm condition monitoring and performance engineering. Skills which are highly sought after as the wind industry continues to grow rapidly.

Research project overview:

Condition monitoring is a crucial aspect of understanding wind turbine drivetrain health and minimising unscheduled downtime due to component failure. Specific wind turbine component faults are typically detected and diagnosed using a range of analytical and signal processing techniques that utilise SCADA, vibration and oil debris data. This research will initially focus on how to bring multiple data streams together effectively to assess component health holistically. The aim is by doing so, a more accurate prognosis can be determined to estimate remaining useful life and optimise decisions for both component inspections and replacement recommendations.

 The research will involve the following steps:

  1. Reliability study of RES fleet, with a focus on detecting and understanding potential environmental and operational drivers for a range of faults
  2. Model dependencies and relationships through time between components and diagnostics for a range of faults throughout the drivetrain
  3. Determine the best data fusion techniques to combine various data streams to develop next generation of component health indicators
  4. Develop a prognosis model to assess component health with the purpose of supporting both short-term and long-term maintenance recommendations
  5. Feasibility study of incorporating other emerging data sources into prognosis models

Entry Requirements

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. Applicants are encouraged to apply with the following experience:

Essential:

  • Data analytics
  • Programming (Python, SQL)
  • Machine learning

Desirable:

  • Wind turbine technology
  • Vibration analysis
  • Signal processing
  • Database architecture
  • Statistics

At RES we celebrate difference as we know it makes our company a great place to work. Encouraging applicants with different backgrounds, ideas and points of view, we create teams who work together to solve complex problems and design practical solutions for our clients. Our multiple perspectives come from many sources including the diverse ethnicity, culture, gender, nationality, age, sex, sexual orientation, gender identity and expression, disability, marital status, parental status, education, social background and life experience of our people.

Applying

To apply, please follow the application link below. The closing date for applications is 01/07/2022

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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: 

For further enquiries related to the EngD research topic contact:  or


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