Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox

Predicting the remaining useful life (RUL) of critical subassemblies can provide an advanced maintenance strategy for wind turbines installed in remote regions. This paper proposes a novel prognostic approach to predict the RUL of bearings in a wind turbine gearbox. An artificial neural network (NN)...

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Published inEnergies (Basel) Vol. 10; no. 1; p. 32
Main Authors Teng, Wei, Zhang, Xiaolong, Liu, Yibing, Kusiak, Andrew, Ma, Zhiyong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2017
Subjects
bearing in gearbox
Bearings
Gearboxes
Life prediction
Mathematical models
Neural networks
Polynomials
prognostic
remaining useful life (RUL)
Remote regions
Strategy
Subassemblies
Training
Turbines
Useful life
Wind power
wind turbine
Wind turbines
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Summary:Predicting the remaining useful life (RUL) of critical subassemblies can provide an advanced maintenance strategy for wind turbines installed in remote regions. This paper proposes a novel prognostic approach to predict the RUL of bearings in a wind turbine gearbox. An artificial neural network (NN) is used to train data-driven models and to predict short-term tendencies of feature series. By combining the predicted and training features, a polynomial curve reflecting the long-term degradation process of bearings is fitted. Through solving the intersection between the fitted curve and the pre-defined threshold, the RUL can be deduced. The presented approach is validated by an operating wind turbine with a faulty bearing in the gearbox.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1996-1073
1996-1073
DOI:10.3390/en10010032