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A Review of Fault Diagnosis Techniques Applied to Aircraft Air Data Sensors

Authors Lucas Lima Lopes , Louise Travé-Massuyès , Carine Jauberthie, Guillaume Alcalay

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Author Details

Lucas Lima Lopes
  • Airbus Operations S.A.S, Toulouse, France
  • LAAS-CNRS, Toulouse, France
Louise Travé-Massuyès
  • LAAS-CNRS, Université de Toulouse, CNRS, France
Carine Jauberthie
  • LAAS-CNRS, Université de Toulouse, UPS, France
Guillaume Alcalay
  • Airbus Operations S.A.S, Toulouse, France

Funding

This work was carried out as part of a CIFRE PhD project in collaboration between the LAAS-CNRS laboratory and AIRBUS. It is partially supported by 3IA Artificial and Natural Intelligence Toulouse Institute (ANITI), French ”Investing for the Future - PIA3” program under the Grant agreement "ANR-19-PI3A-0004"

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Lucas Lima Lopes, Louise Travé-Massuyès, Carine Jauberthie, and Guillaume Alcalay. A Review of Fault Diagnosis Techniques Applied to Aircraft Air Data Sensors. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.3

Abstract

Air data sensors provide essential measurements to ensure the availability of autopilot and to maintain aircraft performance, flight envelope protection and optimal aerodynamic surfaces control laws. The importance of these sensors imply the existence of embedded fault tolerance features, mainly represented by hardware redundancy. The latter is prone to fail in case of common fault of multiple sensors, especially if the faults are coherent and simultaneous. Increasing the robustness of fault detection and isolation (FDI) techniques for air data sensors to the aforementioned conditions is essential for the development of more autonomous aircraft, reducing crew workload and guaranteeing flight protections under adverse conditions. This paper reviews recent works on Air Data System (ADS) FDI, assessing proposed model, data and signal-driven approaches. We finally argue in favor of data-driven and hybrid approaches for the development of virtual sensors and semi-supervised anomaly detectors, offering an overview of ways forward.

Subject Classification

ACM Subject Classification
  • Applied computing → Avionics
Keywords
  • air data
  • FDI
  • aeronautics
  • review
  • survey
  • diagnostics
  • fault

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