Real Time Platform for Monitoring Durability Testing of Aircraft Landing Gear

Resumo

This paper presents an endurance Post-Processing Platform for monitoring durability and resistance testing of aeronautical mechanical and hydraulic systems, focusing on aircraft landing gear. The platform integrates data acquisition and monitoring to ensure consistency and compliance of the bench test and components under examination. Automated MATLAB® scripts capture critical parameters such as operational pressure, return pressure, extension and retraction line pressures, door load, aerodynamic drag load, landing gear angle, and temperatures of hydraulic lines and ambient conditions.
Descriptive statistical analyses, including histograms and box plots, are conducted to provide a deep understanding of the laboratory tests and determine the percentage of the test cycle within predefined acceptable limits. Multivariate analysis discerns correlations between test parameters, aiding in the development of machine learning algorithms for classifying and/or predicting anomalous behaviors.
It is shown that the integration of real-time monitoring, detailed statistical analysis, and machine learning enhances test accuracy and reliability. The preliminary results point out that this methodology optimizes testing processes, reduces diagnostic time, and lowers costs by proactively identifying potential issues early. It is understood that the current research can contribute significantly to improving the reliability, maintainability, and availability (RMA) of aeronautical systems, aligning with the standards of scientific literature in mathematical, computational, mechanical, and aeronautical engineering.