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Prediction of Rolling Bearing Cage Dynamics Using Dynamics Simulations and Machine Learning Algorithms

Title data

Schwarz, Sebastian ; Grillenberger, Hannes ; Tremmel, Stephan ; Wartzack, Sandro:
Prediction of Rolling Bearing Cage Dynamics Using Dynamics Simulations and Machine Learning Algorithms.
In: Tribology Transactions. (2021) . - pp. 1-23.
ISSN 1547-397X
DOI: https://doi.org/10.1080/10402004.2021.1934618

Abstract in another language

Cage instability or highly dynamic cage movement can have a strong influence on the performance of rolling bearings. In addition to very loud and disturbing noises (“squealing”), bearing failure due to cage fracture can occur.

This publication deals with two topics: the general classification of cage motions on the one hand and the prediction of application-dependent cage motions to prevent cage instability during operation on the other hand. Therefore, the dependencies of the unstable cage movement on the bearing’s load and geometric characteristics of the cage are analyzed using a large number of sophisticated simulations, based on multi-body dynamics. To evaluate the cage movements, first a key figure called "Cage Dynamics Indicator" (CDI) is introduced, which is used to classify the simulation results by means of quadratic discriminant analysis into three types “unstable”, “stable” and “circling” (= classification of cage motion). Second, a machine learning algorithm trained and tested on the basis of more than 4 000 simulation results enables a time-efficient prediction of the physical correlations between bearing load and cage properties and the resulting cage dynamics (= prediction of cage motion). A comparison of the calculated cage dynamics with the results of an optical measurement of the cage dynamics rounds off this article. This comparison illustrates the high quality of the simulation models and the training data used for machine learning.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: rolling bearing dynamics; dynamics simulation; cage instability; digital image correlation; machine learning
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: No
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 10 Jun 2021 11:33
Last Modified: 10 Jun 2021 11:33
URI: https://eref.uni-bayreuth.de/id/eprint/65754