Machine Learning for Gearbox Fault Prediction by Using Both Scada and Modeled Data

Lindy Williams, Arch Desai, Yi Guo, Shawn Sheng, Caleb Phillips

Research output: NRELPresentation


This presentation outlines the work in the paper titled "Prognostics of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data" published by the PHM Society and presented at its 2020 annual conference. It is accessible at The technical work is on machine learning approaches for prognostics for gearbox faults. The methodology combines SCADA time series data and physics domain modeling data, derived from the models developed by the NREL team, as inputs to machine learning models to predict gearbox bearing failures with one month lead time. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score, and AUC (area under receiver operating characteristic curve). Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision, and F1 score, and AUC is about 33%, and 12%, and 6% respectively, based on the best modeling case in this study.
Original languageAmerican English
Number of pages20
StatePublished - 2021

Publication series

NamePresented at the Drivetrain Reliability Collaborative Annual Meeting 2021, 16-17 February 2021

NREL Publication Number

  • NREL/PR-2C00-79167


  • bearing
  • gearbox
  • machine learning
  • prognostics
  • wind turbine


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