A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences

Christopher Schnur (), Payman Goodarzi, Yannick Robin, Julian Schauer and Andreas Schütze
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Christopher Schnur: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany
Payman Goodarzi: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany
Yannick Robin: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany
Julian Schauer: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany
Andreas Schütze: Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany

Data, 2025, vol. 10, issue 5, 1-12

Abstract: In practical machine learning (ML) applications, covariate shifts and dependencies can significantly impact model robustness and prediction quality, leading to performance degradation under distribution shifts. In industrial settings, it is crucial to account for covariates during the design of experiments to ensure reliable generalization. The presented dataset of undamaged and artificially damaged cylindrical roller bearings is designed to address the lack of data resources for targeting domain and distribution shifts in this field. The dataset considers multiple key covariates, including mounting position, load, and rotational speed. Each covariate consists of multiple levels optimized for group-based cross-validation. This allows the user to exclude specific groups in the training to validate and test the algorithm. Using this approach, algorithms can be evaluated for their robustness and the effect on the model caused by distribution shifts, allowing their generalization capabilities to be studied under realistic conditions.

Keywords: machine learning; robust learning; domain shift; bearing dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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