2024 Source Code https://fordatis.fraunhofer.de/handle/fordatis/378
http://dx.doi.org/10.24406/fordatis/322

Data for the prediction of the friction torque of scaled blade bearings in a test rig using machine learning

Blechschmidt, Eike  ; Hohmann, Michael  ; Menck, Oliver (Fraunhofer-Institut für Windenergiesysteme IWES)

IWES Fraunhofer-Institut für Windenergiesysteme
Files in This Item:
FileDescriptionSizeFormat 
2024-01-09 Friction torque paper.zip17,03 GBZIPDownload/Open

Abstract

Measurement data and python scripts for machine learning models of the friction torque in a test rig for scaled blade bearings ("BEAT1.1"). Measurement data is given as .parquet-files for several different bearing combinations. A Jupyter Notebook for the models is included.

Technical Information

.parquet-files for the data can be read with Python. The main code can be executed with Jupyter Notebook.
Keywords
Wind energy
Friction torque
Machine learning
Funder
Bundesministerium für Wirtschaft und Klimaschutz BMWK (Deutschland)
Fraunhofer Group
Energietechnologien und Klimaschutz
Show full item record


This item is licensed under a Creative Commons License Creative Commons