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

A Study on Engine Health Monitoring in the Frequency Domain

[+] Author and Article Information
S. Borguet

Turbomachinery Group, University of Liège, Liège 4000, Belgiums.borguet@ulg.ac.be

M. Henriksson

Performance and Control Systems, Volvo Aero Corporation AB, Trollhättan 461 81, Swedenmattias.mh.henriksson@volvo.com

T. McKelvey

Signals and Systems, Chalmers University of Technology, Göteborg 412 96, Swedentomas.mckelvey@chalmers.se

O. Léonard

Turbomachinery Group, University of Liège, Liège 4000, Belgiumo.leonard@ulg.ac.be

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

J. Eng. Gas Turbines Power 133(8), 081604 (Apr 08, 2011) (8 pages) doi:10.1115/1.4002832 History: Received July 08, 2010; Revised July 16, 2010; Published April 08, 2011; Online April 08, 2011

Most of the techniques developed to date for module performance analysis rely on steady-state measurements from a single operating point to evaluate the level of deterioration of an engine. One of the major difficulties associated with this estimation problem comes from its underdetermined nature. It results from the fact that the number of health parameters exceeds the number of available sensors. Among the panel of remedies to this issue, a few authors have investigated the potential of using data collected during a transient operation of the engine. A major outcome of these studies is an improvement in the assessed health condition. The present study proposes a framework that formalizes this observation for a given class of input signals. The analysis is performed in the frequency domain, following the lines of system identification theory. More specifically, the mean-squared estimation error is shown to drastically decrease when using transient input signals. This study is conducted with an engine model representative of a commercial turbofan.

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Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Contribution of the static and dynamic parts of the measurement signal

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Figure 2

Turbofan layout with station numbering and health parameter location

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Figure 3

Effect of the nonlinearity in the engine model

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Figure 4

Effect of the heat transfers in the engine model

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Figure 5

Rank of the FIM versus the frequency and amplitude of the input signal

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Figure 6

Condition number of the FIM versus the frequency and amplitude of the input signal

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Figure 7

Trace of the inverse of the FIM versus the frequency and amplitude of the input signal

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Figure 8

Trace of the inverse of the FIM versus the frequency at particular amplitudes; this graph is extracted from Fig. 7, the frequency axis here is on a logarithmic scale

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Figure 9

Gain of the transfer function between fuel flow and each sensor versus frequency

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Figure 10

Singular value of the system versus the frequency

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