Frequency-Domain Feature Analysis for Early Gear Damage Detection in Planetary Gearboxes
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
https://orcid.org/0000-0003-2034-5092
Martin Dazer
Abstract
The earliest possible detection of pitting damage in gearboxes is a central objective of vibration-based condition monitoring. Machine learning enables the automated analysis of vibration signals, but reliable detection of very early pitting damage requires a detailed understanding of which frequency ranges and frequency resolutions contain damage-relevant information. This work applies machine learning as a data-driven analysis tool to systematically quantify the relevance of frequency-based vibration features for pitting damage detection and pitting size classification. The investigations are based on experiments with three identical single-stage planetary gearboxes and four defined pitting sizes ranging from 0.5 % to 4 %. The measured time signals are transformed into the frequency domain using the Fast Fourier Transform, and the amplitudes of individual frequency bins are used as features. Since the bin width depends on the selected segment length, the influence of frequency resolution on the identification of damage-relevant features is also analyzed. A tree-based gradient boosting algorithm is used for classification, and the importance of individual frequency features is quantified by permutation analysis. The evaluation follows a two-stage approach. First, healthy and damaged states are compared to identify generally relevant frequency features. Second, the healthy state is contrasted separately with each pitting size to determine when specific features become relevant and how their importance changes with increasing damage size. In addition, feature consistency across operating conditions, sensor positions, and the three identical gearboxes is examined. The results support the targeted selection of frequency-based features for subsequent machine-learning-based damage detection and damage size classification and provide guidance for sensor placement, frequency resolution, and measurement system design in application-oriented condition monitoring.
How to Cite
##plugins.themes.bootstrap3.article.details##
Feature Analysis, Damage Detection, Machine Learning, Condition Monitoring, Planetary Gearbox, Vibration Data
Altmann, A., Toloşi, L., Sander, O., & Lengauer, T. (2010). Permutation importance: A corrected feature importance measure. Bioinformatics, 26(10), 1340–1347. doi: 10.1093/bioinformatics/btq134
Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79. doi: 10.1214/09-SS054
Bertsche, B., & Dazer, M. (2022). Zuverlässigkeit im Fahrzeug- und Maschinenbau: Ermittlung von Bauteil- und System-Zuverlässigkeiten. Springer. doi: 10.1007/978-3-662-65024-0
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., & Kasneci, G. (2024). Deep neural networks and tabular data: A survey. IEEE Transactions on Neural Networks and Learning Systems, 35(6), 7499–7519. doi: 10.1109/TNNLS.2022.3229161
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. doi: 10.1023/A:1010933404324
Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh-dimensional feature space. Journal of the Royal Statistical Society Series B: Statistical Methodology, 70(5), 849–911. doi: 10.1111/j.1467-9868.2008.00674.x
FVA Forschungvereinigung Antriebstechnik e.V., & Binanzer, L. (2026). FVA Heft 1698, FVA1036I, Sensorkonzept und Datenverarbeitung mittels künstlicher Intelligenz zur Frühsterkennung von Schäden und deren Lokalisation in Getriebeanwendungen. In Forschungvereinigung Antriebstechnik e.V. (Ed.). Frankfurt.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. doi: 10.1214/aos/1013203451
Friedman, J. H., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28, 337–407. doi: 10.1214/aos/1016218223
Gretzinger, Y., Lucan, K., Stoll, C., & Bertsche, B. (2020). Lifetime extension of gear wheels using an adaptive operating strategy. In Proceedings of IRF2020: 7th International Conference Integrity-Reliability-Failure (pp. 703–710).
Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? In Advances in Neural Information Processing Systems 35 (pp. 507–520). doi: 10.52202/068431-0037
Harris, F. J. (1978). On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 66(1), 51–83. doi: 10.1109/PROC.1978.10837
ISO International Organization for Standardization. (2016). ISO 6336-5: Calculation of load capacity of spur and helical gears—Part 5: Strength and quality of materials (ISO 6336-5:2016(E)).
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In I. Guyon, U. von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 30 (pp. 3149–3157).
Liu, Z., Zhao, X., Zuo, M. J., & Xu, H. (2014). Feature selection for fault level diagnosis of planetary gearboxes. Advances in Data Analysis and Classification, 8, 377–401. doi: 10.1007/s11634-014-0168-4
Randall, R. B. (2021). Basic signal processing techniques. In R. B. Randall, Vibration-based condition monitoring (pp. 53–96). Wiley. doi: 10.1002/9781119477631.ch3
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. doi: 10.1111/j.2517-6161.1974.tb00994.x
Wang, J., Li, S., Xin, Y., & An, Z. (2019). Gear fault intelligent diagnosis based on frequency-domain feature extraction. Journal of Vibration Engineering & Technologies, 7, 159–166. doi: 10.1007/s42417-019-00089-1
Wang, Z., Huang, H., & Wang, Y. (2021). Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification. Measurement, 173, 108654. doi: 10.1016/j.measurement.2020.108654
Welch, B. L. (1947). The generalization of “Student’s” problem when several different population variances are involved. Biometrika, 34(1–2), 28–35. doi: 10.1093/biomet/34.1-2.28
Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70–73. doi: 10.1109/TAU.1967.1161901
WITTENSTEIN alpha GmbH. (2025). alpha Advanced Line SP+: Technical data sheets. WITTENSTEIN alpha GmbH. Retrieved from https://www.wittenstein.de/download/alpha-advanced-line-sp-de.pdf
Zuber, N., & Bajrić, R. (2020). Gearbox faults feature selection and severity classification using machine learning. Eksploatacja i Niezawodność – Maintenance and Reliability, 22(4), 748–756. doi: 10.17531/ein.2020.4.19
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.