LAPSE:2019.0440
Published Article
LAPSE:2019.0440
Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox
Wei Teng, Xiaolong Zhang, Yibing Liu, Andrew Kusiak, Zhiyong Ma
March 26, 2019
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.
Keywords
bearing in gearbox, prognostic, remaining useful life (RUL), wind turbine
Suggested Citation
Teng W, Zhang X, Liu Y, Kusiak A, Ma Z. Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox. (2019). LAPSE:2019.0440
Author Affiliations
Teng W: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China; Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University,
Zhang X: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
Liu Y: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China; Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University,
Kusiak A: Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, USA
Ma Z: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
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Journal Name
Energies
Volume
10
Issue
1
Article Number
E32
Year
2016
Publication Date
2016-12-31
Published Version
ISSN
1996-1073
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Original Submission
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PII: en10010032, Publication Type: Journal Article
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LAPSE:2019.0440
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doi:10.3390/en10010032
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Mar 26, 2019
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CC BY 4.0
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Mar 26, 2019
 
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Original Submitter
Calvin Tsay
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