TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics, and Instrumentation

Integration of On-Line and Off-Line Diagnostic Algorithms for Aircraft Engine Health Management

[+] Author and Article Information
Takahisa Kobayashi

 ASRC Aerospace Corporation, 21000 Brookpark Road, Cleveland, OH 44135

Donald L. Simon

U.S. Army Research Laboratory, Glenn Research Center, 21000 Brookpark Road, Cleveland, OH 44135

The algorithm given by Eqs. 11,12 is equivalent to the maximum a posteriori estimator (15-16) and the minimum variance estimator (15,17).

J. Eng. Gas Turbines Power 129(4), 986-993 (May 01, 2007) (8 pages) doi:10.1115/1.2747640 History: Received April 26, 2007; Revised May 01, 2007

This paper investigates the integration of on-line and off-line diagnostic algorithms for aircraft gas turbine engines. The on-line diagnostic algorithm is designed for in-flight fault detection. It continuously monitors engine outputs for anomalous signatures induced by faults. The off-line diagnostic algorithm is designed to track engine health degradation over the lifetime of an engine. It estimates engine health degradation periodically over the course of the engine’s life. The estimate generated by the off-line algorithm is used to “update” the on-line algorithm. Through this integration, the on-line algorithm becomes aware of engine health degradation, and its effectiveness to detect faults can be maintained while the engine continues to degrade. The benefit of this integration is investigated in a simulation environment using a nonlinear engine model.

Copyright © 2007 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 1

Influence of health degradation and faults

Grahic Jump Location
Figure 2

Architecture of integrated diagnostic approach

Grahic Jump Location
Figure 3

Actual and estimated health degradation over the engine’s lifetime

Grahic Jump Location
Figure 4

Influence of engine health degradation on steady-state values of WSSRHKF and WSSROBEM



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