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DC ElementWertSprache
dc.contributor.advisorMey, Oliver-
dc.contributor.advisorNeudeck, Willi-
dc.contributor.advisorSchneider, André-
dc.contributor.advisorEnge-Rosenblatt, Olaf-
dc.contributor.authorMey, Oliver-
dc.contributor.authorNeudeck, Willi-
dc.contributor.authorSchneider, André-
dc.contributor.authorEnge-Rosenblatt, Olaf-
dc.date.accessioned2020-03-31T06:16:05Z-
dc.date.available2020-03-31T06:16:05Z-
dc.date.created2020-03-25-
dc.date.issued2020-03-25-
dc.identifier.urihttps://fordatis.fraunhofer.de/handle/fordatis/151-
dc.identifier.urihttp://dx.doi.org/10.24406/fordatis/65-
dc.description.abstractThis dataset contains vibration data recorded on a rotating drive train. This drive train consists of an electronically commutated DC motor and a shaft driven by it, which passes through a roller bearing. With the help of a 3D-printed holder, unbalances with different weights and different radii were attached to the shaft. Besides the strength of the unbalances, the rotation speed of the motor was also varied. This dataset can be used to develop and test algorithms for the automatic detection of unbalances on drive trains. Datasets for 4 differently sized unbalances and for the unbalance-free case were recorded. The vibration data was recorded at a sampling rate of 4096 values per second. Datasets for development (ID "D[0-4]") as well as for evaluation (ID "E[0-4]") are available for each unbalance strength. The rotation speed was varied between approx. 630 and 2330 RPM in the development datasets and between approx. 1060 and 1900 RPM in the evaluation datasets. For each measurement of the development dataset there are approx. 107min of continuous measurement data available, for each measurement of the evaluation dataset 28min. Overview of the dataset components: ID Radius [mm] Mass [g] 0D/ 0E - - 1D/ 1E 14 3.281 2D/ 2E 18.5 3.281 3D/ 3E 23 3.281 4D/ 4E 23 6.614en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectvibration analysisen
dc.subjectrotating machineryen
dc.subjectdrive trainen
dc.subjectunbalance detectionen
dc.subjectmachine learningen
dc.subjectdeep learningen
dc.subjectHidden Markov Modelen
dc.titleVibration measurements on a rotating shaft at different unbalance strengthsen
dc.typeTabular Dataen
dc.description.technicalinformationThe dataset contains one csv file per measurement. All files are packed into one zip-file. The files can, for example, be read using Python and the modules zipfile and pandas. An example code for this follows: import pandas as pd import zipfile with zipfile.ZipFile(filename, 'r') as f: with f.open('0D.csv', 'r') as c: data0D = pd.read_csv(c) with f.open('0E.csv', 'r') as c: data0E = pd.read_csv(c) with f.open('1D.csv', 'r') as c: data1D = pd.read_csv(c) with f.open('1E.csv', 'r') as c: data1E = pd.read_csv(c) with f.open('2D.csv', 'r') as c: data2D = pd.read_csv(c) with f.open('2E.csv', 'r') as c: data2E = pd.read_csv(c) with f.open('3D.csv', 'r') as c: data3D = pd.read_csv(c) with f.open('3E.csv', 'r') as c: data3E = pd.read_csv(c) with f.open('4D.csv', 'r') as c: data4D = pd.read_csv(c) with f.open('4E.csv', 'r') as c: data4E = pd.read_csv(c)en
dc.title.translatedVibrationsmessungen an einer rotierenden Welle bei verschiedenen Unwuchtstärkende
fordatis.instituteIIS Fraunhofer-Institut für Integrierte Schaltungen - Institutsteil Entwicklung Adaptiver Systeme EASen
fordatis.rawdatafalseen
Enthalten in den Sammlungen:IIS Fraunhofer-Institut für Integrierte Schaltungen - Institutsteil Entwicklung Adaptiver Systeme EAS

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
fraunhofer_eas_dataset_for_unbalance_detection_v1.zipTabular Data in csv format packed into one zip-file2,61 GBcsv; compressed into one zip archiveÖffnen/Download

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3 fordatis/151.3 2022-11-17 15:39:08.463 Andere Wissenschaftler haben angefragt, ob sie die Daten eines Zwischenergebnisses ebenfalls herunterladen können. Diese sollen deshalb zum Datensatz hinzugefügt werden.
2 fordatis/151.2 2020-03-31 08:44:16.279
1 fordatis/151 2020-03-31 08:16:05.0

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