Remaining useful life and lifetime extension of wind turbine drivetrains

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Title
Remaining useful life and lifetime extension of wind turbine drivetrains

CoPED ID
54e86b27-0dec-4bdf-a380-ee8da67b44fc

Status
Active

Funders

Value
No funds listed.

Start Date
April 30, 2020

End Date
April 29, 2024

Description

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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 estimate 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 fusing the aforementioned multiple sources of data can increase the confidence of a maintenance action under a condition-based monitoring scheme.

This EngD will focus on the wind turbine drivetrain fault detection, isolation and remaining useful life esti-mation using advanced signal processing and machine learning methods and considering component de-pendencies. The work will involve the following:
*Research of various signal processing methods applicable to wind turbine vibration signals.
*Extraction of health indicators from multiple data sources/streams.
*Research of various applicable machine learning methods for prediction using the extracted indica-tors as features.
*Model dependencies between drivetrain components.
*Develop a multi-component degradation model.
*Model lifetime extension scenarios.

The work will be validated using 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.

University of Strathclyde LEAD_ORG
Natural Power STUDENT_PP_ORG

James Carroll SUPER_PER
Georgios Kampolis STUDENT_PER

Subjects by relevance
  1. Machine learning
  2. Wind energy
  3. Forecasts
  4. Wind turbines
  5. Signal processing
  6. Wind power stations
  7. Turbines
  8. State of health
  9. Public health service
  10. Decision making

Extracted key phrases
  1. Wind turbine drivetrain fault detection
  2. Wind turbine vibration signal
  3. Wind turbine maintenance
  4. Useful life esti
  5. Drivetrain component
  6. Signal processing method applicable
  7. Model lifetime extension scenario
  8. Wind farm
  9. Applicable machine learning method
  10. Future health state
  11. Multiple datum source
  12. Advanced signal processing
  13. Component degradation model
  14. Collaborative research project
  15. Strathclyde research team

Related Pages

UKRI project entry

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