Assessing Aircraft Engine Wear through Simulation Techniques

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Published Nov 5, 2024
Abdellah Madane Jérôme Lacaille Florent Forest Hanane Azzag Mustapha Lebbah

Abstract

In the field of aeronautical engineering, understanding and simulating aircraft engine performance is critical, especially for improving operational safety, efficiency, and sustainability. At Safran Aircraft Engines, we were able to demonstrate the effectiveness of using time series collected from the engines after each flight to build a digital twin that provides a dynamic virtual model able to mirror the real engine’s state by using a transformer-based conditional generative adversarial network. This virtual representation allows for advanced simulations under diverse operational scenarios like flight conditions and controls, aiding in understanding the impact of different factors on engine health. It is, therefore, possible for us to provide virtual flights performed by our engines in their actual state of wear. This research paper presents a machine learning model that effectively simulates and monitors the state of aircraft engines in real-time, enabling us to track the evolution of the engines’ health over their life cycle. The model’s adaptability to incorporate new data ensures its applicability throughout the engine’s lifespan, marking a step forward in proactive aeronautic maintenance and potentially enhancing engine longevity through timely diagnostics and interventions.

How to Cite

Madane, A., Lacaille, J. ., Forest, F., Azzag, H., & Lebbah, M. (2024). Assessing Aircraft Engine Wear through Simulation Techniques. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.3924
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Keywords

simulation, cgan, transformer, phm, wear and tear, degradation modeling, aircraft engines, aeronautic

References
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Madane, A., Dilmi, M.-D., Forest, F., Azzag, H., Lebbah, M., & Lacaille, J. (2023). Transformerbased conditional generative adversarial network for multivariate time series generation. In International workshop on temporal analytics@ pakdd 2023. doi: https://pakdd2023.org/wpcontent/ uploads/2023/05/pakdd23w1p2.pdf

Madane, A., & Lacaille, J. (2023). Simulation of the behaviour of engines in their current state of wear. In Proceedings of the international conference on condition monitoring and asset management (Vol. 2023, p. 1-11). The British Institute of Non-Destructive Testing. doi:
10.1784/cm2023.5d2
Section
Technical Research Papers

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