An alternative neural airspeed computation method for aircrafts

An alternative neural airspeed computation method for aircrafts PurposeThis paper aims to present an alternative airspeed computation method based on artificial neural networks (ANN) without requiring pitot-static system measurements.Design/methodology/approachThe data set used to train proposed neural model is obtained from the Digital Flight Data Acquisition Unit records of a Boeing 737 type commercial aircraft for real flight routes. The proposed method uses the flight parameters as inputs of the ANN. The Levenberg–Marquardt training algorithm was used to train the neural model.FindingsThe predicted airspeed values obtained with ANN are in good agreement with the measured airspeed values. The proposed neural model can be used as an alternative airspeed computation method.Practical implicationsThe proposed alternative airspeed computation method can be used when the air data computer or pitot-static system has failed.Originality/valueThe proposed method uses flight parameters as inputs for the ANN. As such, airspeed is calculated using flight parameters instead of the pitot-static system measurements. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aircraft Engineering and Aerospace Technology Emerald Publishing

An alternative neural airspeed computation method for aircrafts

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Publisher
Emerald Group Publishing Limited
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1748-8842
D.O.I.
10.1108/AEAT-10-2015-0228
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to present an alternative airspeed computation method based on artificial neural networks (ANN) without requiring pitot-static system measurements.Design/methodology/approachThe data set used to train proposed neural model is obtained from the Digital Flight Data Acquisition Unit records of a Boeing 737 type commercial aircraft for real flight routes. The proposed method uses the flight parameters as inputs of the ANN. The Levenberg–Marquardt training algorithm was used to train the neural model.FindingsThe predicted airspeed values obtained with ANN are in good agreement with the measured airspeed values. The proposed neural model can be used as an alternative airspeed computation method.Practical implicationsThe proposed alternative airspeed computation method can be used when the air data computer or pitot-static system has failed.Originality/valueThe proposed method uses flight parameters as inputs for the ANN. As such, airspeed is calculated using flight parameters instead of the pitot-static system measurements.

Journal

Aircraft Engineering and Aerospace TechnologyEmerald Publishing

Published: Mar 5, 2018

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