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This is a Quantitative Study study.

A Bearing Health Assessment Technique Based on UMAP and K-Medoids Clustering

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This method shows effective failure detection and health index computation for bearings, indicating improved assessment capabilities.

The proposed method accurately assesses bearing health by integrating UMAP and K-medoids clustering.
Using 18 features, the UMAP algorithm reduces dimensionality while preserving essential health-related information.
K-medoids clustering identifies key stages of bearing health, revealing early failures that are often hard to detect.
Experimental results indicate that this approach enables precise quantitative assessment of bearing degradation over time.

Cheng et al. (2025) studied this question.

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