Machine Learning Analysis in the Diagnostics of the Dynamics of Ball Bearing with Different Radial Internal Clearance

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


Interpretation of acceleration time-series from rolling-element bearings is sometimes challenging if no prior knowledge of the system is available. The evaluation must adapt operational conditions or the actual value of operational parameters. In our analysis, we apply the machine learning methods and statistical indicators in the diagnosis of dynamical response in the self-aligning ball bearing with different radial internal clearance. Machine learning methods are applied for the quantification of the acceleration time-series with statistical indicators and their assignation to the specific state or clearance value. The results of the analysis allow recognizing the bearing’s condition and the clearance value based on experimental acceleration time-series. Additionally, confusion matrices are presented for showing the accuracy of proposed methods. The results of applied Machine Learning methods are on the level of around 80% in classifying the dynamical response to the specific radial clearance. The motivation of the research is to introduce it to on-site practice in the test rig.

TitelRecent Trends in Wave Mechanics and Vibrations : Proceedings of WMVC 2022
HerausgeberZuzana Dimitrovová, Rodrigo Gonçalves, Paritosh Biswas, Tiago Silva
Anzahl der Seiten8
VerlagSpringer Schweiz
ISBN (Print)978-3-031-15757-8
ISBN (elektronisch)978-3-031-15758-5
PublikationsstatusErschienen - 2023
Veranstaltung10th International Conference on Wave Mechanics and Vibrations - WMVC 2022 - HOTEL VIP EXECUTIVE ZURIQU, Lisbon, Portugal
Dauer: 04.07.202206.07.2022
Konferenznummer: 10

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© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.