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Using Machine Learning Methods for Predicting Cage Performance Criteria in an Angular Contact Ball Bearing

Title data

Schwarz, Sebastian ; Grillenberger, Hannes ; Graf-Goller, Oliver ; Bartz, Marcel ; Tremmel, Stephan ; Wartzack, Sandro:
Using Machine Learning Methods for Predicting Cage Performance Criteria in an Angular Contact Ball Bearing.
In: Lubricants. Vol. 10 (2022) Issue 2 . - No. 25.
ISSN 2075-4442
DOI: https://doi.org/10.3390/lubricants10020025

Abstract in another language

Rolling bearings have to meet the highest requirements in terms of guidance accuracy, energy efficiency, and dynamics. An important factor influencing these performance criteria is the cage, which has different effects on the bearing dynamics depending on the cage’s geometry and bearing load. Dynamics simulations can be used to calculate cage dynamics, which exhibit high agreement with the real cage motion, but are time-consuming and complex. In this paper, machine learning algorithms were used for the first time to predict physical cage related performance criteria in an angular contact ball bearing. The time-efficient prediction of the machine learning algorithms enables an estimation of the dynamic behavior of a cage for a given load condition of the bearing within a short time. To create a database for machine learning, a simulation study consisting of 2000 calculations was performed to calculate the dynamics of different cages in a ball bearing for several load conditions. Performance criteria for assessing the cage dynamics and frictional behavior of the bearing were derived from the calculation results. These performance criteria were predicted by machine learning algorithms considering bearing load and cage geometry. The predictions for a total of 10 target variables reached a coefficient of determination of R2≈0.94 for the randomly selected test data sets, demonstrating high accuracy of the models.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Engineering Science > Chair Engineering Design and CAD > Chair Engineering Design and CAD - Univ.-Prof. Dr.-Ing Stephan Tremmel
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 15 Feb 2022 07:18
Last Modified: 15 Feb 2022 07:18
URI: https://eref.uni-bayreuth.de/id/eprint/68678