An insight into wind turbine planet bearing fault prediction using SCADA data

Sofia Koukoura, James Carroll, Alasdair McDonald

Research output: Contribution to journalConference Contributionpeer-review

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Condition based maintenance is being adopted into the decision making process of wind farms, in order to reduce operation costs. SCADA systems are integrated in wind turbines, providing low frequency operational data and are increasingly being used in condition monitoring. The aim of this paper is to explore how can wind turbine gearbox components be monitored using SCADA data. The proposed methodology utilises 10-minute averaged data. Data preprocessing is applied using a clustering filter in order to improve prediction confidence. Normal behaviour models are used to predict potential faults. The efficacy of the proposed methodology is demonstrated with a case study using SCADA data from three operating wind turbines that have a double planetary stage gearbox. Historic data is collected for more than a year before the occurrence of a bearing failure on a planet of the first planetary stage. The case study results indicate the potential importance of generator speed estimation for planet bearing faults. A successful prediction of the bearing health state can be performed through this model and some insight is given into into the optimal SCADA sensors utilization for this type of failure mode.
Original languageEnglish
JournalProceedings of the European Conference of the PHM Society
Issue number1
Publication statusPublished - 30 Jun 2018
EventFourth European Conference of the PHM Society - Utrecht, Netherlands
Duration: 3 Jul 20186 Jul 2018


  • wind turbines
  • SCADA data
  • fault prediction
  • gearbox


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