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This article classifies the dynamic response of rolling bearings in terms of radial internal
clearance values. The value of the radial internal clearance in rolling-element bearings cannot be
described in a deterministic manner, which shows the challenge of its detection through the analysis
of the bearing’s dynamics. In this article, we show the original approach to its intelligent detection
through the analysis of short-time intervals and the calculation of chosen indicators, which can be
assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings
of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal
clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried
out based on the time series of vibrations recorded by the accelerometer and then digitally processed.
Window statistical indicators widely used in the diagnosis of rolling bearings, which served as
features for the machine learning models, were calculated. The accuracy of the classification turned
out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series
processing, which allows for the extraction of subsequent dominant frequencies into experimental
modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the
modes allowed for an increase in classification accuracy to over 90%.