Machine Learning Analysis in the Diagnostics of the Dynamics of Ball Bearing with Different Radial Internal Clearance
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Recent Trends in Wave Mechanics and Vibrations : Proceedings of WMVC 2022. ed. / Zuzana Dimitrovová; Rodrigo Gonçalves; Paritosh Biswas; Tiago Silva. Cham: Springer Schweiz, 2023. p. 599-606 (Mechanisms and Machine Science; Vol. 125).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Machine Learning Analysis in the Diagnostics of the Dynamics of Ball Bearing with Different Radial Internal Clearance
AU - Ambrożkiewicz, Bartłomiej
AU - Syta, Arkadiusz
AU - Gassner, Alexander
AU - Georgiadis, Anthimos
AU - Litak, Grzegorz
AU - Meier, Nicolas
N1 - Conference code: 10
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Machine learning
KW - Radial internal clearance
KW - Rolling-element bearing
KW - Statistical indicators
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85141786239&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/3bd7e3c7-0248-3701-9c92-4bd2af122847/
U2 - 10.1007/978-3-031-15758-5_61
DO - 10.1007/978-3-031-15758-5_61
M3 - Article in conference proceedings
AN - SCOPUS:85141786239
SN - 978-3-031-15757-8
T3 - Mechanisms and Machine Science
SP - 599
EP - 606
BT - Recent Trends in Wave Mechanics and Vibrations
A2 - Dimitrovová, Zuzana
A2 - Gonçalves, Rodrigo
A2 - Biswas, Paritosh
A2 - Silva, Tiago
PB - Springer Schweiz
CY - Cham
T2 - 10th International Conference on Wave Mechanics and Vibrations - WMVC 2022
Y2 - 4 July 2022 through 6 July 2022
ER -