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Aircraft air data system fault detection and reconstruction scheme design

Aircraft air data system fault detection and reconstruction scheme design 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.Design/methodology/approachReal 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.FindingsNo 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.Practical implicationsThe presented alternative analytical redundant airspeed and altitude calculation scheme could be used when the pitot-static system contains any fault condition.Originality/valueInstead 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aircraft Engineering and Aerospace Technology Emerald Publishing

Aircraft air data system fault detection and reconstruction scheme design

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1748-8842
DOI
10.1108/aeat-01-2021-0018
Publisher site
See Article on Publisher Site

Abstract

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.Design/methodology/approachReal 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.FindingsNo 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.Practical implicationsThe presented alternative analytical redundant airspeed and altitude calculation scheme could be used when the pitot-static system contains any fault condition.Originality/valueInstead 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.

Journal

Aircraft Engineering and Aerospace TechnologyEmerald Publishing

Published: Aug 12, 2021

Keywords: Aircraft sensor; Fault detection; Reconstruction; Machine learning

References