Advancing Light Aircraft Health Monitoring with Flight Phase Clustering
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
https://orcid.org/0000-0003-3687-3703
Jan Papuga Sylvain Kubler
Abstract
This paper addresses the clustering of flight phases of a light aircraft for health monitoring using vibration data. The aim is to improve diagnostic and prognostic functions. Grouping condition monitoring data under similar operating conditions is significant for predictive maintenance. Clustering also supports advanced analytics for fault detection and estimation of remaining life. The proposed framework uses self-organizing maps for flight phase clustering. The findings show that the algorithm can recognize and classify flight phases in various operational domains. Additionally, visualization of cluster maps uncovers complex patterns and non-linear relationships in sensor data under different flight conditions. As a followup, analyzing the vibration properties within these estimated clusters (regimes) provides insights from condition monitoring data behavior during flight phases. The results confirm the effectiveness of the method, but also confirm that determining light aircraft regimes requires more focus due to their unique flight patterns that are absent in commercial airliners. In this context, this research has dealt with these unique patterns and provided the foundation for a new model for clustering with an attempt to contribute valuable insights into improving the reliability and efficiency of light aircrafts.
How to Cite
##plugins.themes.bootstrap3.article.details##
Flight Phase Identification, Vibration Data Analysis, Adaptive Machine Learning, Aircraft Health Monitoring
2. Ali, J. B., Saidi, L., Harrath, S., Bechhoefer, E.,&Benbouzid, M. (2018). Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning. Applied Acoustics, 132, 167–181.
3. Amirat, Y., Benbouzid, M. E. H., Al-Ahmar, E., Bensaker, B., & Turri, S. (2009). A brief status on condition monitoring and fault diagnosis in wind energy conversion systems. Renewable and sustainable energy reviews, 13(9), 2629–2636.
4. Barbe, K., Pintelon, R., & Schoukens, J. (2009). Welch method revisited: nonparametric power spectrum estimation via circular overlap. IEEE Transactions on signal processing, 58(2), 553–565.
5. Bektas, O. (2023). Visualising flight regimes using self-organising maps. The Aeronautical Journal, 127(1316), 1817–1831.
6. Bektash, O., & la Cour-Harbo, A. (2020). Vibration analysis for anomaly detection in unmanned aircraft. In Annual conference of the prognostics and health management society 2020.
7. Chen, B., Matthews, P. C., & Tavner, P. J. (2015). Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition. IET Renewable Power Generation, 9(5), 503–513.
8. Gavrilovski, A., Jimenez, H., Mavris, D. N., Rao, A. H., Shin, S., Hwang, I., & Marais, K. (2016). Challenges and opportunities in flight data mining: A review of the state of the art. AIAA Infotech@ Aerospace, 0923.
9. Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480.
10. Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1-3), 1–6.
11. Kohonen, T. (2013). Essentials of the self-organizing map. Neural networks, 37, 52–65.
12. Kordestani, M., Orchard, M. E., Khorasani, K., & Saif, M. (2023). An overview of the state of the art in aircraft prognostic and health management strategies. IEEE Transactions on Instrumentation and Measurement, 72, 1–15.
13. Lyu, Z., Thapa, P., & Desell, T. (2024). Minimally supervised topological projections of self-organizing maps for phase of flight identification. arXiv preprint arXiv:2402.11185.
14. Matthews, B. (n.d.). Dashlink - sample flight data. NASA. Retrieved from https://c3.ndc.nasa.gov/dashlink/projects/85/
15. Oehling, J., & Barry, D. J. (2019). Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data. Safety science, 114, 89–104.
16. Proakis, J. G. (2001). Digital signal processing: principles algorithms and applications. Pearson Education India.
17. Saidi, L., Ali, J. B., Bechhoefer, E., & Benbouzid, M. (2017). Wind turbine high-speed shaft bearings health prognosis through a spectral kurtosis-derived indices and svr. Applied Acoustics, 120, 1–8.
18. Smith III, J. O. (2011). Spectral audio signal processing. W3K publishing. 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.
19. Wittek, P., & Gao, S. (n.d.). Introduction - somoclu 1.7.5 documentation. Retrieved from https://somoclu.readthedocs.io/en/stable/ Yang, W., Tavner, P., & Wilkinson, M. (2009). Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. IET Renewable Power Generation, 3(1), 1–11.
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.