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

A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach

[+] Author and Article Information
S. Borguet

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

O. Léonard

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

In addition, νk and ϵk are assumed uncorrelated.

Note that the residuals rk are also a linear function of wk and bk.

A Brite/Euram project for On-Board Identification, Diagnosis and Control of Turbofan Engine.

J. Eng. Gas Turbines Power 130(2), 021605 (Mar 05, 2008) (7 pages) doi:10.1115/1.2772637 History: Received May 04, 2007; Revised May 07, 2007; Published March 05, 2008

Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the discrepancies between the model prediction and the measurements are zero-mean, white random variables. However, this assumption is not verified when instrumentation (sensor) faults occur. As a result, the identified health parameters tend to diverge from their actual values, which strongly deteriorates the diagnosis. The purpose of this contribution is to blend robustness against sensor faults into a tool for performance monitoring of jet engines. To this end, a robust estimation approach is considered and a sensor-fault detection and isolation module is derived. It relies on a quadratic program to estimate the sensor faults and is integrated easily with the original diagnosis tool. The improvements brought by this robust estimation approach are highlighted through a series of typical test cases that may be encountered on current turbine engines.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Health parameter and state variable update mechanism using a DEKF

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Figure 2

Huber’s weighting function for a scalar random variable

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Figure 3

Integration of the SFDI module

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Figure 4

Turbofan layout with station numbering and health parameter’s location

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Figure 5

Fuel flow profile

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Figure 6

Identification of the bias on p30

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Figure 7

Identification of a positive drift on T260

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Figure 8

Identification of the health parameters with a positive sensor drift on T260. Dotted lines show actual parameter values.

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Figure 9

Misidentification of the sensor drift on T60

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Figure 10

Identification of the health parameters with a sensor drift on T60. Dotted lines show actual parameter values.

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