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Research Papers: Gas Turbines: Aircraft Engine

An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

[+] Author and Article Information
Donald L. Simon

NASA Glenn Research Center,
21000 Brookpark Road, MS 77-1,
Cleveland, OH 44135

Jeffrey B. Armstrong

ASRC Aerospace Corporation,
21000 Brookpark Road, MS 500-ASRC,
Cleveland, OH 44135

This decision was made in an attempt to strike a balance between the dual objectives of performance estimation and fault diagnostics. Accurate turbomachinery deterioration estimation is necessary for performance trend monitoring purposes. Conversely, from a fault isolation standpoint, accurate actuator bias estimation is secondary to the ability to discriminate between different fault types.

Estimated sensor measurements y produced by the Kalman filter are used instead of actual sensor measurements y to conduct this comparison. The estimated values track measured values well and are less noisy, thus providing superior isolation performance.

Contributed by Aircraft Engine Committee of ASME for the Journal of Engineering for Gas Turbines and Power. Manuscript received November 21, 2012; final manuscript received February 22, 2013; published online June 12, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 135(7), 071203 (Jun 12, 2013) (10 pages) Paper No: GTP-12-1447; doi: 10.1115/1.4023902 History: Received November 21, 2012; Revised February 22, 2013

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

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Figures

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Fig. 1

Ground station performance trend monitoring and gas path diagnostic process

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Fig. 2

Gradual versus rapid performance shifts

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Fig. 3

On-board performance trend monitoring and gas path diagnostic process (original proposed design)

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Fig. 4

On-board performance trend monitoring and gas path diagnostic process (simplified proposed design)

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Fig. 5

Example of Kalman filter design 2 health parameter estimation during fan fault (top) and HPT fault (bottom)

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