Cart (Loading....) | Create Account
Close category search window

Neural networks-based reconstruction of flight data for aircraft crash investigations

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Napolitano, M.R. ; Dept. of Mech. & Aerosp. Eng., West Virginia Univ., Morgantown, WV, USA ; Windon, D.A., II.

An the investigation of an aircraft crash the flight recorder data, the voice recorder tracks, and the inspection of the aircraft wreckage are key elements. At times these elements are not sufficient to allow the investigators to reach a conclusion about a cause or a set of causes for the failure(s) leading to the crash. Federal rules do not mandate for a “black box” to record deflections of the aircraft control surfaces. Nevertheless, it is clear that these dynamic time histories can provide critical information leading to the discovery of the cause(s) of the crash. In this paper a neural network approach is proposed for the reconstruction of the time histories of the control surfaces deflections. The neural inputs are the discretized “black box” data; the neural outputs are the estimates of the control surface deflections which minimize weighted quadratic differences between the time histories data from a parallel simulation code and the actual “black box” data. The approach is iterative in nature; starting from an initial time instant, the signal reconstruction scheme will proceed to the next time instant only when the estimates of the control surfaces deflections at the current instant minimize the weighted quadratic differences relative to the next time instant. The results relative to the reconstruction of the control surfaces deflections for a single type of failure under different conditions are presented and discussed

Published in:

Neural Networks, 1996., IEEE International Conference on  (Volume:4 )

Date of Conference:

3-6 Jun 1996

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.