Pattern mining based data fusion for wind turbine condition monitoring

Research output: Chapter in Book/Report/Conference proceedingConference paperResearch


The profitability of wind turbine energy production is for an important part determined by the operation and maintenance costs of wind turbines. An important driver of these costs is currently the premature failure of components due to excessive wear. If it would be possible to accurately predict these failures, preventive maintenance can be made more effective, which should result in less downtime and expensive unexpected failures. This in turn should lower the operational and maintenance costs. The research presented here is a contribution to the research on condition monitoring and failure prediction for wind turbines. To this end, a methodology for failure prediction is designed that combines (fuses) multiple information sources (i.e. SCADA and status log data). The novelty of this research lies in the fact that pattern mining techniques are used to identify relevant rules for a rule-based failure classifier. The methodology is validated on generator bearing failure cases from a real operational wind farm. The results show that the methodology is able to predict generator bearing failures accurately well in advance. The rules on which the predictions are based are interpretable and correspond in general to expert knowledge on the matter.
Original languageEnglish
Title of host publicationJournal of Physics: Conference Series
PublisherIOP Publishing
Number of pages13
Publication statusPublished - 21 Jun 2023

Publication series

NameJournal of Physics: Conference Series
ISSN (Print)1742-6588

Bibliographical note

Funding Information:
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen received funding from the Flemish Government (AI Research Program). The research presented in this paper is partly financed by the European Union (H2020 PLATOON, Pr. No: 872592). The authors would also like to acknowledge FWO for the support through the SBO Robustify project (S006119N), and VLAIO ICON project Supersized 4.0.

Publisher Copyright:
© 2023 Institute of Physics Publishing. All rights reserved.


  • condition monitoring
  • wind turbine
  • pattern mining
  • data fusion


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