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dc.contributor.authorBlechschmidt, Eike-
dc.contributor.authorHohmann, Michael-
dc.contributor.authorMenck, Oliver-
dc.date.accessioned2024-01-17T15:33:20Z-
dc.date.available2024-01-17T15:33:20Z-
dc.date.issued2024-
dc.identifier.urihttps://fordatis.fraunhofer.de/handle/fordatis/378-
dc.identifier.urihttp://dx.doi.org/10.24406/fordatis/322-
dc.description.abstractMeasurement 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.en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectWind energyen
dc.subjectFriction torqueen
dc.subjectMachine learningen
dc.titleData for the prediction of the friction torque of scaled blade bearings in a test rig using machine learningen
dc.typeSource Codeen
dc.contributor.funderBundesministerium für Wirtschaft und Klimaschutz BMWK (Deutschland)en
dc.description.technicalinformation.parquet-files for the data can be read with Python. The main code can be executed with Jupyter Notebook.en
fordatis.groupEnergietechnologien und Klimaschutzen
fordatis.instituteIWES Fraunhofer-Institut für Windenergiesystemeen
fordatis.rawdatafalseen
fordatis.sponsorship.projectid03EE3065en
fordatis.sponsorship.projectid0324344Aen
fordatis.sponsorship.projectnameDevelopment of a methodology for the creation of digital twins of rotor blade bearings for condition monitoringen
fordatis.sponsorship.projectnameIntelligent Blade Bearing Amplitude Controlen
fordatis.sponsorship.projectacronymViBes4Winden
fordatis.sponsorship.projectacronymiBACen
Enthalten in den Sammlungen:Fraunhofer-Institut für IWES

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