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Evolution of Surface Defects on Ball Screw Drive Spindles for intelligent Prognostics and Health Management Systems

Schlagenhauf, Tobias 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)

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). ... mehr

Abstract (englisch):

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). ... mehr


Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Forschungsdaten
Publikationsdatum 12.01.2023
Erstellungsdatum 24.06.2022
Identifikator DOI: 10.5445/IR/1000148057
KITopen-ID: 1000148057
Lizenz Creative Commons Namensnennung – Nicht kommerziell 4.0 International
Schlagwörter Ball Screw Drives; Condition Monitoring; Prognostics and Health Management (PHM); Machine Learning; Intelligent Manufacturing
Liesmich

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.

Art der Forschungsdaten Dataset
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