Condition Monitoring Method for the Gearboxes of Offshore Wind Turbines Based on Oil Temperature Prediction
Zhixin Fu,
Zihao Zhou (),
Junpeng Zhu and
Yue Yuan
Additional contact information
Zhixin Fu: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Zihao Zhou: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Junpeng Zhu: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Yue Yuan: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Energies, 2023, vol. 16, issue 17, 1-17
Abstract:
Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring.
Keywords: SCADA; fault diagnosis; LightGBM; XGBoost; IOWA (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://www.mdpi.com/1996-1073/16/17/6275/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/17/6275/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:17:p:6275-:d:1227939
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().