Landing Gear Health Assessment: Synergising Flight Data Analysis with Theoretical Prognostics in a Hybrid Assessment Approach

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jun 27, 2024
Haroun El Mir Stephen King Martin Skote Mushfiqul Alam Simon Place

Abstract

This study addresses a critical shortfall in aircraft landing gear (LG) maintenance: the challenge of detecting degradation that necessitates intervention between scheduled maintenance intervals, particularly in the absence of hard landings. To address this issue, we introduce a Performance Degradation Metric (PDM) utilising Flight Data Recorder (FDR) output during the touchdown and initial roll phases of landing. This metric correlates time-series accelerometer data from a Saab 340B aircraft’s onboard sensors with non-linear response dynamic models that predict expected LG travel and reaction profiles across a set of ground contact cycles within a single landing. This facilitates the early detection of deviations from standard LG response behaviour, pinpointing potential performance abnormalities. The initiator of this approach is the Landing Sequence Typology, which systematically decomposes each aircraft landing into successive dynamic periods defined by their representative boundary conditions. What follows is the setting of initial parameters for the ordinary differential equations (ODE)s of motion that determine the orientation and impact responses of the most critical components of the LG assembly. Solving these ODEs with the integration of a non-linear representation of an oleo-pneumatic shock absorber model compliant with CS25 aircraft standards produces anticipated profiles of LG travel based on factors such as aircraft weight and speed at touchdown, which are subsequently cross-referenced with real accelerometer data, enhanced by video footage analysis. This footage is crucial for verifying the sequence of LG touchdowns and corresponding accelerometer outputs, thereby bolstering the precision of our analysis. Upon the conclusion of this study, by facilitating the early identification of LG performance deviations in specific landing scenarios, this diagnostic tool shall enable timely maintenance interventions. This proactive approach not only mitigates the risk of damage escalation to other components but also transitions main LG maintenance practices from reactive to proactive.

How to Cite

El Mir, H., King, S., Skote, M. ., Alam, M., & Place, S. (2024). Landing Gear Health Assessment: Synergising Flight Data Analysis with Theoretical Prognostics in a Hybrid Assessment Approach. PHM Society European Conference, 8(1), 10. https://doi.org/10.36001/phme.2024.v8i1.4085
Abstract 326 | PDF Downloads 297

##plugins.themes.bootstrap3.article.details##

Keywords

Landing Gear Health Assessment, flight data analysis, theoretical prognostics, hybrid assessment, simulation, landing sequence typology

References
Alam, M., Whidborne, J., & Westwood, M. (2024). Navigation Algorithm for a Twin-Engine Turboprop Aircraft using an Extended Kalman Filter. EuroGNC 2024 Forum, Bristol, UK [to be published]

Bakunowicz, J., & Rzucidło, P. (2020). Detection of Aircraft Touchdown Using Longitudinal Acceleration and Continuous Wavelet Transformation. Sensors, 20(24), 7231. https://doi.org/10.3390/s20247231

Chang, Y.-J., Hsu, H.-K., Hsu, T.-H., Chen, T.-T., & Hwang, P.-W. (2023). The Optimization of a Model for Predicting the Remaining Useful Life and Fault Diagnosis of Landing Gear. Aerospace, 10(11), 963. https://doi.org/10.3390/aerospace10110963

Currey, N. S. (1988). Aircraft landing gear design: Principles and practices. American Institute of Aeronautics and Astronautics.

De Martin, A., Jacazio, G., & Sorli, M. (2022). Simulation of Runway Irregularities in a Novel Test Rig for Fully Electrical Landing Gear Systems. Aerospace, 9(2), Article 2. https://doi.org/10.3390/aerospace9020114

Delebarre, Grondel, Dupont, Rouvarel, & Yoshida. (2017). Wireless monitoring system for lightweight aircraft landing gear. 2017 International Conference on Research and Education in Mechatronics (REM), 1–6. https://doi.org/10.1109/REM.2017.8075230

Hsu, T.-H., Chang, Y.-J., Hsu, H.-K., Chen, T.-T., & Hwang, P.-W. (2022). Predicting the Remaining Useful Life of Landing Gear with Prognostics and Health Management (PHM). Aerospace, 9(8), 462. https://doi.org/10.3390/aerospace9080462

Jan R. Wright, & E. Cooper, J. (2014). Ground Manoeuvres. In Introduction to Aircraft Aeroelasticity and Loads (pp. 337–365). https://doi.org/10.1002/9781118700440.ch15

Kaplan, M. P., Willis, T., & Wolff, T. A. (1997). Damage Tolerance Assessment of CASA Landing Gear. 972626. https://doi.org/10.4271/972626

Martin, A. D., Jacazio, G., Parisi, V., & Sorli, M. (2022). Prognosis of Wear Progression in Electrical Brakes for Aeronautical Applications. PHM Society European Conference, 7(1), 329–337. https://doi.org/10.36001/phme.2022.v7i1.3353

SAE International. (2017). AIR4894: Landing Gear Stability. SAE International. https://saemobilus.sae.org/content/AIR4894/

Schmidt, R. K. (2008). Monitoring of aircraft landing gear structure. The Aeronautical Journal, 112(1131), 275–278. https://doi.org/10.1017/S0001924000002220

Schmidt, R. K. (2021). The Design of Aircraft Landing Gear. SAE International.

Wu, D., Gu, H., & Liu, H. (2007). GA-Based Model Predictive Control of Semi-Active Landing Gear. Chinese Journal of Aeronautics, 20(1), 47–54. https://doi.org/10.1016/S1000-9361(07)60006-5

Zhang, H., Wang, S., & Yang, Q. (2018). Research of Monitoring the Landing Gear Damage Based on the Optical Fiber Acoustic Emission Technology. IOP Conference Series: Materials Science and Engineering, 452, 022091. https://doi.org/10.1088/1757-899X/452/2/022091
Section
Technical Papers