Gas Turbines: Controls, Diagnostics, and Instrumentation

In-Flight Isolation of Degraded Engine Components by Shape Comparison of Transient Outputs

[+] Author and Article Information
Jeffrey C. Simmons

Department of Mechanical and Industrial Engineering,  University of Massachusetts, Amherst, MA 01003

Kourosh Danai1

Department of Mechanical and Industrial Engineering,  University of Massachusetts, Amherst, MA 01003danai@ecs.umass.edu


Corresponding author.

J. Eng. Gas Turbines Power 134(6), 061602 (Apr 13, 2012) (11 pages) doi:10.1115/1.4005814 History: Received January 03, 2011; Revised October 13, 2011; Published April 13, 2012; Online April 13, 2012

A direct method of aircraft engine health monitoring is introduced that can isolate a degraded engine component in-flight. The method utilizes continuous wavelet transformations of transient engine sensory data to represent their shape attributes in the time scale domain. This enables contrasting the shapes of the current engine outputs with those previously collected from the engine. Continuous wavelet transforms also provide enhanced delineation of the engine transients in the time scale domain. This enables identification of minute differences between the outputs affected by component degradations and between the sensitivities of modeled outputs with respect to the health parameters or components. The presence of these differences is used in this method as evidence of degradation effects on the outputs and/or parameters or components effects on the outputs. The effectiveness of the proposed method is evaluated in engine simulations. The results indicate that with the suite of outputs currently available on-board 70% to 96% of the degraded components simulated can be isolated for new and older engines.

Copyright © 2012 by American Society of Mechanical Engineers
Topics: Engines , Shapes , Flight
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Figure 1

Schematic diagram of the two-spool, high-bypass, separate flow turbofan engine represented by the NPSS simulation model, together with the station and primary component locations

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Figure 2

Translation and dilation of the Sombrero wavelet across a time signal

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Figure 3

The residuals of the first three outputs representing degradation in the LPC

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Figure 4

The degradation signatures of the LPC by the Gauss WT at ηd  =  1.75 extracted from the residuals in Fig. 3

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Figure 5

Sample residual counterparts of those in Fig. 3 with additive noise in the outputs

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Figure 6

Sample residual counterparts of those in Fig. 3 obtained from biased sensory measurements




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