0
Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

A Generalized Likelihood Ratio Test for Adaptive Gas Turbine Performance Monitoring

[+] Author and Article Information
S. Borguet

Turbomachinery Group, University of Liège, Chemin des Chevreuils, 1 4000 Liège, Belgiums.borguet@ulg.ac.be

O. Léonard

Turbomachinery Group, University of Liège, Chemin des Chevreuils, 1 4000 Liège, Belgiumo.leonard@ulg.ac.be

A domestic object damage is caused by an element of the engine (e.g., part of a blade) that breaks off and strikes a downstream flow path component.

A Brite/Euram project for on-board identification, diagnosis, and control of turbofan engines.

J. Eng. Gas Turbines Power 131(1), 011601 (Oct 13, 2008) (8 pages) doi:10.1115/1.2967493 History: Received April 01, 2008; Accepted April 02, 2008; Published October 13, 2008

Kalman filters are widely used in the turbine engine community for health monitoring purposes. This algorithm has proven its capability to track gradual deterioration with good accuracy. On the other hand, its response to rapid deterioration is a long delay in recognizing the fault and/or a spread of the estimated fault on several components. The main reason for this deficiency lies in the transition model of the parameters that is blended in the Kalman filter and assumes a smooth evolution of the engine condition. This contribution reports the development of an adaptive diagnosis tool that combines a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements a generalized likelihood ratio test in order to detect and estimate an abrupt fault. The enhancement in terms of accuracy and reactivity brought by this adaptive Kalman filter is highlighted for a variety of simulated fault cases that may be encountered on a commercial aircraft engine.

FIGURES IN THIS ARTICLE
<>
Copyright © 2009 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Health parameter update mechanism using an extended Kalman filter

Grahic Jump Location
Figure 2

Integration of the adaptive component with the Kalman filter

Grahic Jump Location
Figure 3

Turbofan layout with station numbering and health parameter location

Grahic Jump Location
Figure 4

Tracking of engine wear with the generic diagnosis tool. In the legends, “est” stands for estimated.

Grahic Jump Location
Figure 5

Tracking of engine wear+faulta with the generic diagnosis tool. In the legends, “est” stands for estimated.

Grahic Jump Location
Figure 6

Tracking of engine wear+faulta with the adaptive algorithm. In the legends, “est” stands for estimated.

Grahic Jump Location
Figure 7

Tracking of engine wear+faultj with the adaptive algorithm. In the legend, “est” stands for estimated.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In