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  4. Prediction of the friction torque of scaled blade bearings in a test rig using machine learning
 
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June 1, 2024
Journal Article
Title

Prediction of the friction torque of scaled blade bearings in a test rig using machine learning

Abstract
Blade bearing friction torque is a required parameter for the design of a pitch actuator, and deviations from a bearing’s initial torque can be utilized for condition monitoring of the bearing. The torque of large-scale bearings can, however, be difficult to predict due to quality fluctuations in the production of these large-scale components. Therefore, this paper employs machine learning approaches to predict the torque of a given set of bearings in a controlled test environment based on measurement data from that same set of bearings. Possible applications of the model include use for condition monitoring by checking for deviations from the bearing’s initial behavior.
Author(s)
Hohmann, Michael
Fraunhofer-Institut für Windenergiesysteme IWES  
Blechschmidt, Eike
Fraunhofer-Institut für Windenergiesysteme IWES  
Hallerberg, S.
Menck, Oliver  
Fraunhofer-Institut für Windenergiesysteme IWES  
Journal
Journal of physics. Conference series  
Project(s)
Entwicklung einer Methodik zur Erstellung digitaler Zwillinge von Rotorblattlagern zur Zustandsüberwachung  
Funder
Bundesministerium für Wirtschaft und Klimaschutz  
Conference
International Conference "The Science of Making Torque from Wind" 2024  
Open Access
DOI
10.1088/1742-6596/2767/5/052010
Language
English
Fraunhofer-Institut für Windenergiesysteme IWES  
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