Machine Learning–Aided Synthetic Air Data System for Commercial Aircraft

Abstract

Air data inertial reference system (ADIRS) parameters are very important for the safe flight of an aircraft. The ADIRS consists of air data inertial reference units (ADIRUs) and the air data reference (ADR) part of the ADIRU, which provides the air data [altitude (ALT), angle of attack (AOA), airspeed, and temperature information] examined in this study. ADR is essential to continuously ensure accurate and precise information to the flight management guidance computers (FMGCs), electronic flight instruments system (EFIS), and other systems on the aircraft for reliable and safe flight operation. This study estimated the ADR parameters (altitude, angle of attack, airspeed, and temperature) to obtain a synthetic air data system for data continuity in the event of any sensor failure on the aircraft using correlated data. According to correlation analysis, the angle of attack, computed airspeed (CAS), and static air temperature (SAT) data have the highest correlation with the stabilizer position (STAB), whereas the altitude data have the highest correlation with the low-pressure engine spool rotational speed (N1). The AOA, CAS, SAT, and ALT parameters were estimated by decision tree, support vector machine, and Gaussian process regression models using real flight data collected from a local airline. The Gaussian process regression model was better at generalizing the data set for data estimation than were the other machine learning methods used in this study. MATLAB version R2023a software was used in all operations.