The purpose of this study is to detect and reconstruct a fault in pitot probe and static ports, which are components of the air data system in commercial aircrafts, without false alarm and no need for pitot-static measurements. In this way, flight crew will be prevented from flying according to incorrect data and aircraft accidents that may occur will be prevented.
Real flight data collected from a local airline was used to design the relevant system. Correlation analysis was performed to select the data related to the airspeed and altitude. Fault detection and reconstruction were carried out by using adaptive neural fuzzy inference system and artificial neural networks, which are machine learning methods. MATLAB software was used for all the calculations.
No false alarm was detected when the fault test following the fault modeling was carried out at 0–2 s range by filtering the residual signal. When the fault was detected, fault reconstruction process was initiated so that system output could be achieved according to estimated sensor data.
The presented alternative analytical redundant airspeed and altitude calculation scheme could be used when the pitot-static system contains any fault condition.
Instead of using the methods based on hardware redundancy, the authors designed a new system within the scope of this study. Fault situations that may occur in pitot probes and static ports are modeled and different fault scenarios that can be encountered in all flight phases have been examined.
This study was supported by Eskisehir Technical University Scientific Research Projects Commission under the grant no: 20DRP050.
Kilic, U. and Unal, G. (2021), "Aircraft air data system fault detection and reconstruction scheme design", Aircraft Engineering and Aerospace Technology, Vol. 93 No. 6, pp. 1104-1114. https://doi.org/10.1108/AEAT-01-2021-0018
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