Vibration Analysis for Damage Detection and Classification for Condition Monitoring on Worm Gears
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Worm gears exhibit a higher proportion of sliding motion during tooth meshing than spur gears. Typically, this type of gear consists of a soft worm wheel, often made of brass, and a hard steel worm. These characteristics promote the occurrence of abrasive wear and further fatigue damage during operation. The objective of this study is the vibration data analysis for a sensor-based detection and classification of two types of damage in worm gears during operation. For this purpose, a data set is available containing the measurement data from an accelerometer under various operating conditions with regard to rotational speed and torque. The data set comprises measurements of the undamaged condition, artificial tooth thickness reduction on the worm wheel to reflect wear, and artificial breakouts reflecting pitting damage on the worm. For damage detection the accelerometer data is evaluated in the frequency domain. The amplitudes at different frequencies are analyzed for each type of damage. Both types of damage exhibit distinct characteristics in the analyzed frequency spectra. Based on these differences, vibration-based indicators are derived from the frequency spectra, enabling the detection and classification of breakout damage on the worm and wear damage on the worm wheel. Breakout damage on the worm is characterized by a discrepancy in the acceleration amplitudes at the harmonics of the gear mesh frequency when compared to measurements obtained under undamaged conditions. For the wear damage on the worm wheel, a systematic difference compared to the undamaged reference measurement can be detected in the frequency range between 7.5 kHz and 8.5 kHz. In addition to the effect of the damage, a distinct influence of the rotational speed and torque is observed on the extracted indicators for damage detection. Since different data evaluation methods are best suited for detecting each type of damage, damage classification is possible. In the context of PHM, this enables health management measures to be implemented according to the severity of the detected type of damage.
How to Cite
##plugins.themes.bootstrap3.article.details##
worm gear, damage, condition monitoring, frequency analysis
Elasha, F., Ruiz-Cárcel, C., Mba, D., Kiat, G., Nze, I., & Yebra, G. (2014). Pitting detection in worm gearboxes with vibration analysis. Engineering Failure Analysis, 42, 366–376. doi: 10.1016/j.engfailanal.2014.04.028
Hammami, C., Chakroun, A. E., Chaari, F., Hammami, A., De-Juan, A., Fernandez, A., Viadero, F., & Haddar, M. (2022). Estimation of vibrations levels of a worm gear model with plastic wheel. In Design and Modeling of Mechanical Systems – V (pp. 646–654). doi: 10.1007/978-3-031-14615-2_72
Hizarci, B., Ümütlü, R. C., Kıral, Z., & Öztürk, H. (2021). Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system. SN Applied Sciences, 3(1). doi: 10.1007/s42452-020-04131-w
Hsiao, J. C., Shivam, K., & Kam, T. Y. (2020). Fault diagnosis method for worm gearbox using convolutional network and ensemble learning. Journal of Physics: Conference Series, 1509(1). doi: 10.1088/1742-6596/1509/1/012030
International Standards Organization. (2020). Gears: Calculation of load capacity of worm gears. ISO/TS 14521:2020. Genève, Switzerland: International Standards Organization.
Jbily, D., Guingand, M., & Vaujany, J. (2016). A wear model for worm gear. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(7–8), 1290–1308. doi: 10.1177/0954406215606747
Karabacak, Y., Gürsel, Ö. N., & Gümüşel, L. (2020). Worm gear condition monitoring and fault detection from thermal images via deep learning method. Eksploatacja i Niezawodność – Maintenance and Reliability, 22(3), 544–556. doi: 10.17531/ein.2020.3.18
Karabacak, Y., Gürsel, Ö. N., & Gümüşel, L. (2022). Common spatial pattern-based feature extraction and worm gear fault detection through vibration and acoustic measurements. Measurement, 187. doi: 10.1016/j.measurement.2021.110366
Karabacak, Y., Gürsel, Ö. N., & Gümüşel, L. (2022). Intelligent worm gearbox fault diagnosis under various working conditions using vibration, sound and thermal features. Applied Acoustics, 186. doi: 10.1016/j.apacoust.2021.108463
Opalić, M., Žeželj, D., & Vučković, K. (2015). A new method for description of the pitting process on worm wheels propagation. Wear, 332–333, 1145–1150. doi: 10.1016/j.wear.2015.01.053
Peng, Z., & Kessissoglou, N. (2003). An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear, 255(7–12), 1221–1232. doi: 10.1016/S0043-1648(03)00098-X
Raadnui, S. (2021). Condition monitoring of worm gear wear and wear particle analysis of industrial worm gear sets. Wear, 476. doi: 10.1016/j.wear.2021.203687
Randall, R. B. (2011). Vibration-based condition monitoring. Chichester: Wiley.
Sharif, K. J., Evans, H. P., & Snidle, R. W. (2006). Prediction of the wear pattern in worm gears. Wear, 261(5–6), 666–673. doi: 10.1016/j.wear.2006.01.018
Schnetzer, P. E., Pellkofer, J., & Stahl, K. (2023). Calculation method for wear of steel-bronze rolling-sliding contacts relating to worm gears. Forschung im Ingenieurwesen, 87(3), 961–971. doi: 10.1007/s10010-023-00692-5
Tao, Z., Chen, H., Zhang, X., & Jiang, Y. (2021). Failure analysis of worm gear in worm transmission. Journal of Physics: Conference Series, 1965(1). doi: 10.1088/1742-6596/1965/1/012132
Ümütlü, R. C., Hizarci, B., Öztürk, H., & Kiral, Z. (2016). Pitting detection in a worm gearbox using artificial neural networks. InterNoise16, 6526–6534.
Ümütlü, R. C., Hizarci, B., Ozturk, H., & Kiral, Z. (2020). Classification of pitting fault levels in a worm gearbox using vibration visualization and ANN. Sādhanā, 45(22). doi: 10.1007/s12046-019-1263-1
Vojtko, I., Kočiško, M., Šmeringaiová, A., & Adamčík, P. (2013). Vibration of worm gear boxes. Applied Mechanics and Materials, 308, 45–49. doi: 10.4028/[www.scientific.net/AMM.308.45](http://www.scientific.net/AMM.308.45)
Waqar, T., & Demetgul, M. (2016). Thermal analysis MLP neural network-based fault diagnosis on worm gears. Measurement, 86, 56–66. doi: 10.1016/j.measurement.2016.02.024
Ya-xiong, Z., Ke-jian, M., Yan, L., Lin, Q., Zhan-xiu, L., & Wei-min, Z. (1988). An analysis of the wear features of worm gear tooth flanks. Tribology International, 21(5), 281–285. doi: 10.1016/0301-679X(88)90006-0
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.