Alternativer Identifier:
(KITopen-DOI) 10.5445/IR/1000148057
Verwandter Identifier:
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Ersteller/in:
Schlagenhauf, Tobias [Institut für Produktionstechnik]
Beitragende:
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Titel:
Evolution of Surface Defects on Ball Screw Drive Spindles for intelligent Prognostics and Health Management Systems
Weitere Titel:
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Beschreibung:
(Abstract) The dataset shows the development of 82 surface defects (pits) over the operating time of Ball Screw Drives. The name of the images is structured as follows: XX_XX_YYMMDDHHMMSS_XX_XX. Here X is some identifier, which is not important in this context. The dataset is especially suited to investigate the development of surface defects on ball screw drive spindles. The dataset mainly addresses the machine learning research community for engineering and computer science to build intelligent models for surface defect detection and forecasting in the context of prognostics and health management (PHM). Each folder consists the evolution of one pit.
(Abstract) The dataset shows the development of 82 surface defects (pits) over the operating time of Ball Screw Drives. The name of the images is structured as follows: XX_XX_YYMMDDHHMMSS_XX_XX. Here X is some identifier, which is not important in this context. The dataset is especially suited to investigate the development of surface defects on ball screw drive spindles. The dataset mainly addresses the machine learning research community for engineering and computer science to build intelligent models for surface defect detection and forecasting in the context of prognostics and health management (PHM). Each folder consists the evolution of one pit.
(Technical Remarks) The dataset shows the development of 82 surface defects (pits) over the operating time of Ball Screw Drives. The name of the images is structured as follows: XX_XX_YYMMDDHHMMSS_XX_XX. Here X is some identifier, which is not important in this context. The dataset is especially suited to investigate the development of surface defects on ball screw drive spindles. The dataset mainly addresses the machine learning research community for engineering and computer science to build intelligent models for surface defect detection and forecasting in the context of prognostics and health management (PHM). Each folder consists the evolution of one pit.
Schlagworte:
Ball Screw Drives
Condition Monitoring
Prognostics and Health Management (PHM)
Machine Learning
Intelligent Manufacturing
Zugehörige Informationen:
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Sprache:
-
Erstellungsjahr:
Fachgebiet:
Engineering
Objekttyp:
Dataset
Datenquelle:
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Verwendete Software:
-
Datenverarbeitung:
-
Erscheinungsjahr:
Rechteinhaber/in:
Schlagenhauf, Tobias
Förderung:
-
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Status:
Publiziert
Eingestellt von:
kitopen
Erstellt am:
Archivierungsdatum:
2023-06-22
Archivgröße:
56,4 GB
Archiversteller:
kitopen
Archiv-Prüfsumme:
172faeaed0c61fd1c2522f3a58c4feec (MD5)
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