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.
TechnicalRemarks: 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.
