Modeling of Journal Bearings for Wear Diagnosis and Its Verification Using SVM

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Published Sep 4, 2023
Masaki Goto Tsuyoshi Inoue Akira Heya Keiichi Katayama Shogo Kimura Shigeyuki Tomimatsu Shota Yabui

Abstract

Recently, deterioration of infrastructure facilities has become problem. To reduce inspection costs, wear diagnosis of journal bearings in operating conditions using machine learning has been studied. However, constructing a highly accurate machine learning model requires training data, and it may be difficult to conduct numerous tests depending on the application. In this study, a mathematical model of a rotor system for wear diagnosis of journal bearings was developed, and the vibration data was obtained when the clearance changed due to wear. Then, changes in the features used for condition monitoring were examined. Furthermore, the important features for wear diagnosis were selected and SVM models were constructed to verify the mathematical model.

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Keywords

journal bearing, wear, mathematical model, simulation, machine learning

References
Alves, D. S., Daniel, G. B., Castro, H. F., Machado, T. H., Cavalca, K. L., Gecgel, O., Dias, J. P., & Ekwaro-Osire, S., (2020). Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault, Mechanism and Machine Theory Volume 149, July 2020, 103835 doi:10.1016/j.mechmachtheory.2020.10383

Caesarendra, W. & Tjahjowidodo, T. (2017). A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing, Machines 2017, 5(4), 21 doi:10.3390/machines5040021

Goto, D., Inoue, T., Hori, T., Yabui, S., Katayama, K., Tomimatsu, S., & Heya, A. (2023). Failure diagnosis and physical interpretation of journal bearing for slurry liquid using long-term real vibration data, Structural Health Monitoring, 2023, (16 pages) doi:10.1177/14759217231184579

König, F., Sous, C., Chaib, A. O., & Jacobs, G. (2021). Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems, Tribology International, Volume 155, March 2021, 106811 doi:10.1016/j.triboint.2020.106811

Li, X., Dong, S., & Yuan, Z. (1999). Discrete wavelet transform for tool breakage monitoring, International Journal of Machine Tools and Manufacture Volume 39, Issue 12, December 1999, Pages 1935-1944 doi:10.1016/S0890-6955(99)00021-8

Maldonado, S. & Weber, R. (2009). A wrapper method for feature selection using Support Vector Machines, Information Sciences Volume 179, Issue 13, 13 June 2009, Pages 2208-2217 doi:10.1016/j.ins.2009.02.014

Sun, L., Wang, T., Ding, W., Xu, J., & Lin, Y. (2021). Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification, Information Sciences Volume 578, November 2021, Pages 887-912 doi:10.1016/j.ins.2021.08.032
Section
Special Session Papers