Selection of Reference Circles in the Analysis of Roundness of Rolling Bearings Parts

Захаров, О. В.; Пугин, К. Г.

doi:10.1007/s11018-022-02055-y

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Rolling-element bearings have strongly nonlinear characteristics due to multiple factors such as shape errors [20], defects [21], friction [22], or contamination [23]. In the case of evident fault detection, the Fast Fourier Transform (FFT) easily describes the characteristic frequencies corresponding to the faults of the specific element of a rolling-element bearing [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods

Ambrożkiewicz

Syta

Georgiadis

et al. 2023

Sensors

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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%.

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