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Article

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

1
Department of Mechanical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Engineering Design, Martensstraße 9, 91058 Erlangen, Germany
2
Schaeffler Technologies AG & Co. KG, Industriestraße 1-3, 91074 Herzogenaurach, Germany
3
Faculty of Engineering, Universität Bayreuth, Engineering Design and CAD, Universitätsstr. 30, 95447 Bayreuth, Germany
*
Author to whom correspondence should be addressed.
Lubricants 2022, 10(2), 25; https://doi.org/10.3390/lubricants10020025
Received: 14 December 2021 / Revised: 27 January 2022 / Accepted: 4 February 2022 / Published: 11 February 2022
(This article belongs to the Special Issue Machine Learning in Tribology)
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 R20.94 for the randomly selected test data sets, demonstrating high accuracy of the models. View Full-Text
Keywords: rolling bearing dynamics; cage instability; regression; machine learning; neural networks; random forest; gradient boosting; evolutionary algorithms rolling bearing dynamics; cage instability; regression; machine learning; neural networks; random forest; gradient boosting; evolutionary algorithms
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MDPI and ACS Style

Schwarz, S.; Grillenberger, H.; Graf-Goller, O.; Bartz, M.; Tremmel, S.; Wartzack, S. Using Machine Learning Methods for Predicting Cage Performance Criteria in an Angular Contact Ball Bearing. Lubricants 2022, 10, 25. https://doi.org/10.3390/lubricants10020025

AMA Style

Schwarz S, Grillenberger H, Graf-Goller O, Bartz M, Tremmel S, Wartzack S. Using Machine Learning Methods for Predicting Cage Performance Criteria in an Angular Contact Ball Bearing. Lubricants. 2022; 10(2):25. https://doi.org/10.3390/lubricants10020025

Chicago/Turabian Style

Schwarz, Sebastian, Hannes Grillenberger, Oliver Graf-Goller, Marcel Bartz, Stephan Tremmel, and Sandro Wartzack. 2022. "Using Machine Learning Methods for Predicting Cage Performance Criteria in an Angular Contact Ball Bearing" Lubricants 10, no. 2: 25. https://doi.org/10.3390/lubricants10020025

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