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

A Sparse Estimation Approach to Fault Isolation

[+] Author and Article Information
S. Borguet

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

O. Léonard

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

A Domestic object damage is caused by an element of the engine (e.g., part of a blade) that breaks off and strikes a downstream flow-path component.

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A Brite/Euram project for onboard identification, diagnosis, and control of turbofan engine.

J. Eng. Gas Turbines Power 132(2), 021601 (Nov 04, 2009) (7 pages) doi:10.1115/1.3156815 History: Received March 19, 2009; Revised March 19, 2009; Published November 04, 2009; Online November 04, 2009

Least-squares-based methods are very popular in the jet engine community for health monitoring purposes. In most practical situations, the number of health parameters exceeds the number of measurements, making the estimation problem underdetermined. To address this issue, regularization adds a penalty term on the deviations of the health parameters. Generally, this term imposes a quadratic penalization on these deviations. A side effect of this technique is a relatively poor isolation capability. The latter feature can be improved by recognizing that abrupt faults impact at most one or two component(s) simultaneously. This translates mathematically into the search for a sparse solution. The present contribution reports the development of a fault isolation tool favoring sparse solutions. It is very efficiently implemented in the form of a quadratic program. As a validation procedure, the resulting algorithm is applied to a variety of fault conditions simulated with a generic commercial turbofan model.

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Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Comparison of the penalty induced by different norms

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Figure 2

Turbofan layout with station numbering and parameter location

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Figure 3

Sparse estimate of a hpc fault

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Figure 4

Signatures of SW26R and VSV on the sensors

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Figure 5

Comparison of the sparse and usual least-squares solutions for an lpc fault

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