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Sensor fault detection and reconstruction system for commercial aircrafts

Published online by Cambridge University Press:  17 December 2021

U. Kilic Open the ORCID record for U. Kilic [Opens in a new window]  and
G. Unal Open the ORCID record for G. Unal [Opens in a new window]
U. Kilic*
Affiliation:
Department of Avionics, Erzincan Binali Yildirim University, Erzincan, 24002, Turkey
G. Unal
Affiliation:
Department of Avionics, Eskisehir Technical University, Eskisehir, 26470, Turkey
*
*Corresponding author. Email: [email protected]
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Abstract

The aim morphing of this study is to detect and reconstruct a fault in angle-of-attack sensor and pitot probes that are components in commercial aircrafts, without false alarm and no need for additional measurements. 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 angle-of-attack and airspeed. Fault detection and reconstruction were carried out by using Adaptive Neural Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), which are machine-learning methods. No false alarm was detected when the fault test following the fault modeling was carried out at 0–1 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. Instead of using the methods based on hardware redundancy, we designed a new system within the scope of this study.

Keywords

Aircraft sensorFault detectionReconstructionMachine learning
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1299 , May 2022 , pp. 889 - 905
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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