By Topic

Anomaly detection for health management of aircraft gas turbine engines

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

4 Author(s)
D. Tolani ; Dept. of Mech. Eng., Pennsylvania State Univ., University Park, PA, USA ; M. Yasar ; Shin Chin ; A. Ray

This paper presents a comparison of different pattern recognition algorithms to identify slow time scale anomalies for health management of aircraft gas turbine engines. A new tool of anomaly detection, based on symbolic dynamics and information theory, is compared with traditional pattern recognition tools of principal component analysis (PCA) and artificial neural network (ANN). Time series data of the observed variables on the fast time scale are analyzed at slow time scale epochs for early detection of anomalies. The time series data are obtained from a generic engine simulation model. Health monitoring of gas turbine engines based on these techniques is discussed.

Published in:

Proceedings of the 2005, American Control Conference, 2005.

Date of Conference:

8-10 June 2005