Dynamic Modeling of Distributed Wear-Like Faults in Spur Gears: Simplified Approach with Experimental Validation
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Dynamic models of gears are recognized for offering a promising platform for gaining a profound understanding of the dynamic response, particularly the vibration signature. Wear is considered among the most common and concerning fault mechanisms in gears, yet its recognition and subsequent diagnosis remain challenging. In this study, we introduce an existing dynamic model of spur gear vibrations and extend its validation for distributed wear-like faults. The novelty of this work lies in addressing the complexities associated with modeling distributed faults using simplified yet sophisticated approaches. These involve variance among defected teeth, calculation of time-variant gear mesh stiffness, and consideration of the forces induced by the fault. The model is validated through pioneering controlled experiments, analyzing dozens of degrading distributed wear-like faults. This comparison verifies our capability to generate realistic simulations of vibration signals from worn gears. To bridge the discrepancy between the induced and simulated faults, the model first constructs the healthy profile of the inspected gear, incorporating manufacturing errors and tooth modifications. Subsequently, meticulous photography enables the replication of faults in the model with a high resemblance to the experiment. The results demonstrate a strong correlation between measured and simulated signals, as verified through trend analysis of features extracted from synchronous average signals in both the cycle and order domains. This study lays the groundwork for in-depth investigation into the physics of gear wear, paving the way for potential applications such as fault severity estimation and intelligent fault diagnosis in future studies.
How to Cite
##plugins.themes.bootstrap3.article.details##
dynamic modeling, vibration analysis, gear wear, health monitoring
Dadon, I., Koren, N., Klein, R., & Bortman, J. (2018). A realistic dynamic model for gear fault diagnosis. Engineering Failure Analysis, 84. https://doi.org/10.1016/j.engfailanal.2017.10.012 Feng, K., Ji, J. C., Ni, Q., & Beer, M. (2023). A review of vibration-based gear wear monitoring and prediction techniques. In Mechanical Systems and Signal Processing (Vol. 182). https://doi.org/10.1016/j.ymssp.2022.109605 Liang, X., Zuo, M. J., & Feng, Z. (2018). Dynamic modeling of gearbox faults: A review. Mechanical Systems and Signal Processing, 98, 852–876. https://doi.org/10.1016/J.YMSSP.2017.05.024 Liu, X., Yang, Y., International, J. Z.-T., & 2016, undefined.
(2016). Investigation on coupling effects between surface wear and dynamics in a spur gear system. ElsevierX Liu, Y Yang, J ZhangTribology International, 2016•Elsevier. https://doi.org/10.1016/j.triboint.2016.05.006 Matania, O., Bachar, L., Bechhoefer, E., & Bortman, J.
(2024). Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial. Sensors 2024, Vol. 24, Page 454, 24(2), 454. https://doi.org/10.3390/S24020454 Mohammed, O. D., & Rantatalo, M. (2020). Gear fault models and dynamics-based modelling for gear fault detection – A review. Engineering Failure Analysis, 117, 104798. https://doi.org/10.1016/J.ENGFAILANAL.2020.1047 98 Randall, R. B. (1982). A New Method of Modeling Gear Faults. Journal of Mechanical Design, 104(2), 259267. https://doi.org/10.1115/1.3256334 Ren, J., Coatings, H. Y.-, & 2022, undefined. (2022). A dynamic wear prediction model for studying the interactions between surface wear and dynamic response of spur gears. Mdpi.ComJ Ren, H YuanCoatings, 2022•mdpi.Com. https://doi.org/10.3390/coatings12091250 Shen, Z., Qiao, B., Yang, L., Luo, W., Yan, R., Manufacturing, X. C.-P., & 2020, undefined. (2020). Dynamic modeling of planetary gear set with tooth surface wear. ElsevierZ Shen, B Qiao, L Yang, W Luo, R Yan, X ChenProcedia Manufacturing, 2020•Elsevier. https://doi.org/10.1016/j.promfg.2020.06.010
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.