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

An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation

[+] Author and Article Information
Jonathan S. Litt

US Army Research Laboratory, Glenn Research Center, 21000 Brookpark Road, MS 77-1, Cleveland, OH 44135

J. Eng. Gas Turbines Power 130(1), 011601 (Dec 26, 2007) (12 pages) doi:10.1115/1.2747254 History: Received January 23, 2006; Revised March 02, 2007; Published December 26, 2007

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs, such as thrust. The engine’s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters related to each major engine component. Accurate thrust reconstruction depends on knowledge of these health parameters, but there are usually too few sensors to be able to estimate their values. In this new technique, a set of tuning parameters is determined that accounts for degradation by representing the overall effect of the larger set of health parameters as closely as possible in a least-squares sense. The technique takes advantage of the properties of the singular value decomposition of a matrix to generate a tuning parameter vector of low enough dimension that it can be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters’ ability to facilitate matching of both measured and unmeasured engine outputs, as well as state variables. Additional properties of the formulation are shown to lend themselves well to diagnostics.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

State variables, scaled actual and estimates

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Figure 2

Output parameters, scaled actual and estimates

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Figure 3

Auxiliary parameters, scaled actual and filtered estimates

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Figure 4

Estimated q signals and V*p, demonstrating fault detection

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Figure 5

Estimated health parameter-based tuners

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Figure 6

Fault signal p̂≈V*Tq̂ and actual health parameter shift, demonstrating fault isolation

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