Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure

Alan Turnbull, James Carroll, Alasdair McDonald

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
13 Downloads (Pure)


Reducing downtime through predictive or condition-based maintenance is a promising strategy to help reduce costs associated with wind farm operation and maintenance. To help effectively monitor wind turbine condition, operators now rely on multiply sources of data to make informed operational decisions which can minimise downtime, increasing availability and profitability of any given site. Two of such approaches are SCADA temperature and vibration monitoring, which are typically performed in isolation and compared over time for both fault diagnostics and reliability analysis. Presenting two separate case studies, this paper describes a methodology to bring multiple data sources together to diagnose faults by using a single-class support vector machine classifier to assess normal behaviour model error, with results showing that anomalies can be detected more consistently when compared to more standard approaches of analysing each data source in isolation.

Original languageEnglish
Number of pages15
JournalWind Energy
Early online date4 Oct 2020
Publication statusE-pub ahead of print - 4 Oct 2020


  • anomaly detection
  • condition monitoring
  • failure
  • machine learning
  • vibration
  • wind turbine

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