Wind turbine intelligent gear fault identification: Annual Conference of the PHM Society

Sofia Koukoura, James Carroll, Alasdair McDonald

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

This paper aims to present the development of a framework for monitoring of wind turbine gearboxes and prognosis of gear fracture faults, using vibration data and machine learning techniques. The proposed methodology analyses gear vibration signals in the order domain, using a shaft tachometer pulse. Indicators that represent the health state of the gear are algorithmically extracted. Those indicators are used as features to train diagnostic models that predict the health status of the gear. The efficacy of the proposed methodology is demonstrated with a case study using real wind turbine vibration data. Data is collected for a wind turbine at various time steps prior to failure and according to the maintenance reports there is enough data to form a healthy baseline. The data is classified according to the time before failure that the signal was collected.The learning algorithms used are discussed and their results are compared. The case study results indicate that this data driven model can lay the groundwork for a robust framework for the early detection of emerging gear tooth fracture faults. This can lead to minimisation of wind turbine downtime and revenue increase.
Original languageEnglish
Publication statusPublished - 2 Oct 2017

Keywords / Materials (for Non-textual outputs)

  • wind turbine
  • gearbox
  • gear fracture faults
  • diagnostic


Dive into the research topics of 'Wind turbine intelligent gear fault identification: Annual Conference of the PHM Society'. Together they form a unique fingerprint.

Cite this