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

A Way to Deal With Model-Plant Mismatch for a Reliable Diagnosis in Transient Operation

[+] Author and Article Information
S. Borguet

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

P. Dewallef

Turbomachinery Group, University of Liège, Chemin des Chevreuils 1, 4000 Liège, Belgiump.dewallef@gmail.com

O. Léonard

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

Linearized models are often used to lower the computational burden.

In addition, νk and ϵk are assumed uncorrelated.

Note that another indicator of engine acceleration could be chosen, e.g., core acceleration.

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

Recall that the engine is open loop, fuel flow piloted.

J. Eng. Gas Turbines Power 130(3), 031601 (Mar 26, 2008) (8 pages) doi:10.1115/1.2833491 History: Received June 20, 2006; Revised October 29, 2007; Published March 26, 2008

Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction and the measurements are zero-mean, white, Gaussian random variables. In a turbine engine diagnosis, however, this assumption does not always hold due to the presence of biases in the model. This is especially true for a transient operation. As a result, the estimated parameters tend to diverge from their actual values, which strongly degrades the diagnosis. The purpose of this contribution is to present a Kalman filter diagnosis tool where the model biases are treated as an additional random measurement error. The new methodology is tested on simulated transient data representative of a current turbofan engine configuration. While relatively simple to implement, the newly developed diagnosis tool exhibits a much better accuracy than the original Kalman filter in the presence of model biases.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

Health parameter and state variable update mechanism using a DEKF

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Figure 2

Integration of the BCM

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Figure 3

Turbofan layout with station numbering and health parameter location

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Figure 4

Fuel flow profile for learning set generation

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Figure 5

Model-plant mismatch

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Figure 6

Mean bias extraction for T3

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Figure 7

Fuel flow profile, test bench conditions

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Figure 8

Diagnosis with BCM disabled

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Figure 9

Diagnosis with BCM enabled

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Figure 10

Diagnosis with hybrid BCM

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Figure 11

Fuel flow profile, cruise conditions

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Figure 12

Diagnosis with the corrected BCM, cruise conditions



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