Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study

Lindy Williams, Caleb Phillips, Shawn Sheng, Aron Dobos, Xiupeng Wei

Research output: Contribution to conferencePaperpeer-review

5 Scopus Citations

Abstract

Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.

Original languageAmerican English
Number of pages9
DOIs
StatePublished - Jun 2020
Event2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020 - Detroit, United States
Duration: 8 Jun 202010 Jun 2020

Conference

Conference2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020
Country/TerritoryUnited States
CityDetroit
Period8/06/2010/06/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

NREL Publication Number

  • NREL/CP-2C00-76575

Keywords

  • Generator bearing failures
  • Machine learning
  • Prediction
  • Wind turbine

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