Prediction of wind turbine generator failure using two-stage cluster-classification methodology

Alan Turnbull, James Carroll, Alasdair McDonald, Sofia Koukoura

Research output: Contribution to journalArticlepeer-review


Reducing wind turbine downtime through innovations surrounding asset management has the potential to greatly influence the levelised cost of energy (LCoE) for large wind farm developments. Focusing on generator bearing failure and vibration data, this paper presents a two-stage methodology to predict failure within 1 to 2 months of occurrence. Results are obtained by building up a database of failures and training machine learning algorithms to classify the bearing as healthy or unhealthy. This is achieved by first using clustering techniques to produce subpopulations of data based on operating conditions, which this paper demonstrates can greatly influence the ability to diagnose a fault. Secondly, this work classifies individual clusters as healthy or unhealthy from vibration-based condition monitoring systems by applying order analysis techniques to extract features. Using the methodology explained in the report, an accuracy of up to 81.6% correct failure prediction was achieved.
Original languageEnglish
Pages (from-to)1593-1602
Number of pages10
JournalWind Energy
Issue number11
Publication statusPublished - 30 Nov 2019


  • bearing
  • condition monitoring
  • failure
  • vibration machine learning
  • wind turbine


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