Institute of Driveline Technology (IAA)

Deep-groove Ball Bearing 6314 Dataset

Project History

The dataset presented in this work was acquired within the funded research projects SEED (Smart Efficient Electric Driveline, FKZ 13FH585KX0) and KI-Werkstatt (FEIH_2486678). As part of the project, a comprehensive experimental database is established to provide high-quality reference data for the analysis and validation of bearing fault diagnosis methods. The dataset includes measurements from SKF 6314 deep-groove ball bearings representing healthy operation, laser-induced inner raceway defects, laser-induced outer raceway defects, and corrosion-induced damage. Data are recorded at rotational speeds of 1000 rpm and 2000 rpm under two representative combined load cases of 3257 N radial / 1556 N axial and 4893 N radial / 2337 N axial, derived from the gearbox input torques of 215 Nm and 323 Nm of the SEED axle gearbox via a free-body analysis of the drive shaft. These operating conditions are selected to capture the influence of varying speed and mechanical loading on bearing behavior, resulting in a diverse dataset suitable for investigating fault-related vibration characteristics under representative operating scenarios. For each bearing & load condition, vibration and rotation signals were recorded over a period of 60 s. The data are stored as separate CSV files per second.

Applications of the dataset

The dataset provides a benchmark for the development and evaluation of vibration-based bearing fault diagnosis methods. It is suitable for signal processing studies, feature extraction, conventional machine learning approaches, and deep learning algorithms. Furthermore, the dataset may support research on data augmentation techniques, including generative models, as well as comparative benchmarking of fault detection, fault classification, and anomaly detection methods.