By Topic

Anomaly detection for advanced military aircraft using neural networks

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
$33 $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)
T. Brotherton ; Intelligent Autom. Corp., San Diego, CA, USA ; T. Johnson

Automated Prognostics and Health Management (PHM) is a requirement for the advanced military aircraft. PHM is the key to achieving true condition-based maintenance. PHM processing strategies include modules for the detection, diagnosis and prognosis of known fault conditions. However in real operations there will also occur faults and other off-nominal operations that were never anticipated nor ever encountered before. We call these events anomalies. Missing the presence of an anomaly could potentially be catastrophic with the loss of the pilot and aircraft. We have developed a neural net approach for performing anomaly detection. The neural net anomaly detector `learns' to recognize consistent sets of multiple input sensor signal patterns from known nominal data. It is generic and has been applied to a variety of aircraft subsystems and for fusion with other detectors with excellent results. Presented are a description of the neural net anomaly detector and the application to advanced military aircraft

Published in:

Aerospace Conference, 2001, IEEE Proceedings.  (Volume:6 )

Date of Conference:

2001