Wind Turbine Gearbox Fault Prognosis Using High-Frequency SCADA Data

Ayush Verma, Donatella Zappala, Shawn Sheng, Simon Watson

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations


Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

Original languageAmerican English
Article numberArticle No. 032067
Number of pages11
JournalJournal of Physics: Conference Series
Issue number3
StatePublished - 2 Jun 2022
Event2022 Science of Making Torque from Wind, TORQUE 2022 - Delft, Netherlands
Duration: 1 Jun 20223 Jun 2022

Bibliographical note

Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

NREL Publication Number

  • NREL/JA-5000-81931


  • Gearbox failure prognostics
  • High-frequency SCADA data
  • Machine learning
  • Wind turbine


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