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

A Methodology to Improve the Robustness of Gas Turbine Engine Performance Monitoring Against Sensor Faults

[+] Author and Article Information
Pierre Dewallef

A&M Department,
University of Liège,
Chemin des chevreuils 7 (B49),
4000 Liège, Belgium
e-mail: p.dewallef@ulg.ac.be

Sébastien Borguet

A&M Department,
University of Liège,
Chemin des chevreuils 1 (B52/3),
4000 Liège, Belgium
e-mail: s.borguet@ulg.ac.be

See [2] for a more proper statement of the concept of mathematical expectation of a random variable.

It has to be noted that the present approach is not the most generic methodology used to deduce the Kalman filter. Yet, the interested reader is referred to [8,9] for a more generic approach.

A Brite/Euram project concerned with on-board identification and control of turbofan engines.

This threshold of 0.25% corresponds to 3 times the standard deviation of the estimated health parameters (i.e., the square root of the diagonal terms of the covariance matrix Pw,k).

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the Journal of Engineering for Gas Turbines and Power. Manuscript received July 16, 2012; final manuscript received October 23, 2012; published online April 23, 2013. Assoc. Editor: Allan Volponi.

J. Eng. Gas Turbines Power 135(5), 051601 (Apr 23, 2013) (7 pages) Paper No: GTP-12-1280; doi: 10.1115/1.4007976 History: Received July 16, 2012; Revised October 23, 2012

For turbine engine performance monitoring purposes, system identification techniques are often used to adapt a turbine engine simulation model to some measurements performed while the engine is in service. Doing so, the simulation model is adapted through a set of so-called health parameters whose values are intended to represent a faithful image of the actual health condition of the engine. For the sake of low computational burden, the problem of random errors contaminating the measurements is often considered to be zero mean, white, and Gaussian random variables. However, when a sensor fault occurs, the measurement errors no longer satisfy the Gaussian assumption and the results given by the system identification rapidly become unreliable. The present contribution is dedicated to the development of a diagnosis tool based on a Kalman filter whose structure is slightly modified in order to accommodate sensor malfunctions. The benefit in terms of the diagnostic reliability of the resulting tool is illustrated on several sensor faults that may be encountered on a current turbofan layout.

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References

Figures

Grahic Jump Location
Fig. 1

Performance monitoring tool based on an Kalman filter for the case of a turbofan engine. The Kalman gain is computed using relation [8].

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Fig. 2

One-dimensional examples of sensor malfunctions on a zero-mean Gaussian noise with unitary standard deviation

Grahic Jump Location
Fig. 3

Comparison of the penalization term corresponding the Gaussian assumption and the Huber M estimator

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Fig. 4

The turbofan layout used as an application test case

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