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 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.