Loading [MathJax]/extensions/MathMenu.js
Data-driven Health Monitoring and Anomaly Detection in Aircraft Shock Absorbers | IEEE Conference Publication | IEEE Xplore

Data-driven Health Monitoring and Anomaly Detection in Aircraft Shock Absorbers


Abstract:

Ground handling maneuvers in aircraft are strongly affected by the operational condition of the system. In particular, the shock absorbers present in the Main Landing Gea...Show More

Abstract:

Ground handling maneuvers in aircraft are strongly affected by the operational condition of the system. In particular, the shock absorbers present in the Main Landing Gear may have an incorrect amount of oil and/or gas, which deteriorates their performance and can pose a safety hazard for the pilot. In this paper, different methods are proposed to automatically assess the shock absorber status during ground braking maneuvers while the anti-skid system is active. To study the problem, a validated multibody aircraft simulator in a MATLAB/Simulink environment is used. Different data-driven algorithms and sensor placements for the data collection are proposed and evaluated, leveraging the simulator by conducting braking maneuvers over the operational envelope of the system. It is found that a Gaussian Process Regression model preprocessed by a Principal Component Analysis projection based on measurements of the vertical acceleration of the aircraft's body yields promising results.
Date of Conference: 05-07 June 2023
Date Added to IEEE Xplore: 02 August 2023
ISBN Information:

ISSN Information:

Conference Location: Montreal, QC, Canada

Contact IEEE to Subscribe

References

References is not available for this document.