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Evolution of surface defects on ball screw drive spindles for intelligent prognostics and health management systems

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.

Cite this as

Schlagenhauf, Tobias (2023). Dataset: Evolution of surface defects on ball screw drive spindles for intelligent prognostics and health management systems. https://doi.org/10.35097/1340

DOI retrieved: 2023

Additional Info

Field Value
Imported on August 4, 2023
Last update August 4, 2023
License CC BY-NC 4.0 Attribution-NonCommercial
Source https://doi.org/10.35097/1340
Author Schlagenhauf, Tobias
Source Creation 2023
Publishers
Karlsruhe Institute of Technology
Production Year 2022
Publication Year 2023
Subject Areas
Name: Engineering