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dc.contributor.authorClimaco, Paolo-
dc.contributor.authorGarcke, Jochen-
dc.contributor.authorIza Teran, Victor Rodrigo-
dc.contributor.authorLecei, Ivan-
dc.date.accessioned2022-03-31T03:46:51Z-
dc.date.available2022-03-31T03:46:51Z-
dc.date.issued2022-
dc.identifier.urihttps://fordatis.fraunhofer.de/handle/fordatis/262-
dc.identifier.urihttp://dx.doi.org/10.24406/fordatis/191-
dc.description.abstractFiles containing acceleration signals, representing the vibration response of the modelled gearbox for three full rotations of the shaft. S={5,13} indicates the wind (W) speed considered in the simulation, while X={h,cr} indicates the health condition. 'cr' indicates that the simulation includes the cracked tooth. On the contrary, 'h' indicates that the signal has been produced considering a healthy gearbox and not including damage. Y= {1,2,3} indicates the set of excitation parameters chosen to simulate the signal. The three different excitation parameters (ep) settings used for the experiments can be consulted in Table 4 of the paper. Z={10,13,16} relates to the different crack lengths simulated. Specifically, it indicates the percentage of the dimensionless stiffness function's decrease applied to simulate the tooth crack (tc).en
dc.description.sponsorshipThe dataset has been created as part of the project MADESI, which has been granted by the BMBF within their announcement “Richtlinie zur Förderung von Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens.”en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.subjectWindTurbine Gearboxen
dc.subject.ddcDDC::500 Naturwissenschaften und Mathematiken
dc.titleWind turbine gearbox simulation dataen
dc.typeTabular Dataen
dc.contributor.funderBundesministerium für Bildung und Forschung BMBF (Deutschland)en
dc.description.technicalinformationThe data is created using matlab. The data is is saved in the mat-format.en
fordatis.instituteSCAI Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnenen
fordatis.rawdatatrueen
fordatis.sponsorship.projectid01IS18043Aen
fordatis.sponsorship.projectnameMaschine Lernverfahren für Stochastisch-Deterministische Multi-Sensor Signaleen
fordatis.sponsorship.projectacronymMADESIen
fordatis.date.start2020-
fordatis.date.end2021-
Enthalten in den Sammlungen:Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI


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3 fordatis/262.3 2022-10-24 12:17:26.909 Reviewer des assoziierten Papers möchte gerne mehr Variation in den Daten haben
1 fordatis/262 2022-03-31 05:46:51.0

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