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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
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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 Technology – Emerald Publishing

Published: Aug 12, 2021

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

References

  • Review of methodologies for aircraft sensors fault detection and correction
    Alvarez, O.H.; Zea, L.B.G.; Bil, C.; Napolitano, M.; Fravolini, M.L.
  • Operational use of angle of attack on modern commercial jet airplanes
    Cashman, J.E.; Kelly, B.D.; Nield, B.N.
  • Avionic air data sensors fault detection and isolation by means of singular perturbation and geometric approach
    Castaldi, P.; Mimmo, N.; Simani, S.
  • Gas turbine engines
  • Sensor fault detection and isolation for aircraft control systems by kinematic relations
    Eykeren, L.V.; Chu, Q.P.
  • Fault diagnosis and fault tolerant control design for aerospace systems: a bibliographical review
    Fekih, A.
  • Experimental interval models for the robust fault detection of aircraft air data sensors
    Fravolini, M.L.; Napolitano, M.R.; Core, G.; Papa, U.
  • Air data system fault modeling and detection
    Freeman, P.; Seiler, P.; Balas, G.J.
  • Performance comparison of representative model based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis
    He, Q.; Zhang, W.; Lu, P.; Liu, J.
  • Adaptive observer-based fault detection and active tolerant control for unmanned aerial vehicles attitude system
    Lijia, C.; Yu, T.; Guo, Z.
  • Air data sensor fault detection and diagnosis in the presence of atmospheric turbulence: theory and experimental validation with real flight data
    Lu, P.; Kampen, E.J.V.; Visser, C.; Chu, Q.
  • Evaluation of MCAS system
    Mako, S.; Pilat, M.; Svab, P.; Kozuba, J.; Cicvakova, M.
  • The dramatic effects of pitot static system blockages and failures
    Oliveira, L.R.M.
  • Concurrent aircraft routing and maintenance scheduling using goal programming
    Orhan, I.; Kapanoǧlu, M.; Karakoç, T.H.
  • Enhanced detection and isolation of angle of attack sensor faults
    Ossmann, D.; Joos, H.D.
  • FlightGear as a tool for real time fault injection, detection and self repair
    Purvis, A.; Morris, B.; Mcwilliam, R.
  • Fuel conservation strategies
    Roberson, B.
  • A model based approach for sensor fault detection in civil aircraft control surface
    Sercekman, O.; Kutay, A.T.
  • Simulation of sensor failure accommodation in flight control system of transport aircraft: a modular approach
    Singh, S.; Murthy, T.
  • Neural network based sensor fault detection for flight control systems
    Singh, S.; Vasudeviah, R.M.T.
  • A fault detection and isolation design for a dual pitot tube air data system
    Sun, K.; Gebre, E.D.
  • Neural-sliding mode approach-based adaptive estimation, isolation and tolerance of aircraft sensor fault
    Taimoor, M.; Aijun, L.
  • The effect of flight distance on fuel mileage and CO2 per passenger kilometer
    Turgut, E.T.; Usanmaz, O.; Cavcar, M.
  • An alternative neural airspeed computation method for aircrafts
    Turkmen, I.
  • Aircraft parameter estimation using ELM network
    Verma, H.O.; Peyada, N.K.
  • Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm
    Verma, H.O.; Peyada, N.K.
  • Estimation of aerodynamic parameters near stall using maximum likelihood and extreme learning machine-based methods
    Verma, H.O.; Peyada, N.K.
  • Predicting thrust of aircraft using artificial neural networks
    Yildirim Dalkiran, F.; Toraman, M.
  • Signal and model-based fault detection for aircraft systems
    Zolghadri, A.; Cieslak, J.; Efimov, D.; Henry, D.; Philippe, G.; Dayre, R.; Gheorghe, A.; Berre, H.L.