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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

## Abstract

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

Volponi, A., 2003, “Foundation of Gas Path Analysis (Part I and II),” Gas Turbine Condition Monitoring and Fault Diagnosis (VKI Lecture Series No. 1), von Karman Institute, Rhode Saint-Genèse, Belgium.
Papoulis, A., 1998, Probability, Random Variables, and Stochastic Processes ( Electrical & Electronic Engineering Series), 3rd ed., McGraw-Hill, New York.
Huber, P. J., 1992, Robust Statistics, John Wiley, New York.
Rousseeuw, P., 1984, “Least Median of Squares Regression,” J. Am. Stat. Assoc., 79(388), pp. 871–880.
Ershov, A., 1978, “Robust Filtering Algorithms,” Automat. Telemekhanika, 7, pp. 992–996.
Mitter, S. K., and Shick, I. C., 1965, “Point Estimation, Stochastic Approximation and Robust Kalman Filtering,” Tech. Rep. LIDS-P-2165, Laboratory of Information and Decision Systems, MIT, Cambridge, MA.
Masreliez, C., 1975, “Approximate Non-Gaussian Filtering With Linear State and Observation Relations,” IEEE Trans. Auto. Control, 20(1), pp. 107–110.
Kalman, R., 1960, “A New Approach to Linear Filtering and Prediction Problems,” ASME J. Basic Eng., 82(1), pp. 35–44.
Haykin, S., 2001, “Kalman Filters,” Kalman Filtering and Neural Networks (Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications and Control), Wiley, New York.
Dewallef, P., 2005, “Application of the Kalman Filter to Health Monitoring of Gas Turbine Engines: A Sequential Approach to Robust Diagnosis,” Ph.D. thesis, University of Liège, Wallonia, Belgium.
Kamboukos, P., and Mathioudakis, K., 2003, “Comparison of Linear and Non-Linear Gas Turbine Performance Diagnostics,” ASME Turbo Expo–Controls, Diagnostics and Instrumentation, Atlanta, GA, June 16–19, ASME Paper No. GT2003-38518.
Fuchs, J., 1999, “An Inverse Problem Approach to Robust Regression,” 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, March 15–19, Vol. 4, pp. 1809–1812.
Volponi, A., 2003, “Basic Fault Model and Measurement Error Handling,” Gas Turbine Condition Monitoring and Fault Diagnosis (VKI Lecture Series No. 1), von Karman Institute, Rhode Saint-Genese, Belgium.
Kamboukos, P., and Mathioudakis, K., 2005, “Multipoint Non-Linear Method for Enhanced Component and Sensor Malfunction Diagnosis,” ASME Turbo Expo 2006: Power for Land, Sea, and Air (GT2006), Barcelona, May 8–11, ASME Paper No. GT2006-90451.
Borguet, S., and Léonard, O., 2008, “A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach,” ASME J. Eng. Gas Turbines Power, 130(2), p. 021605.
Stamatis, A., Mathioudakis, K., Ruiz, J., and Curnock, B., 2001, “Real-Time Engine Model Implementation for Adaptive Control and Performance Monitoring of Large Civil Turbofans,” 46th ASME International Gas Turbine & Aeroengine Technical Congress, ASME Turbo Expo 2001–Land, Sea & Air, New Orleans, LA, June 4–7, ASME Paper No. 2001-GT-0362.
Curnock, B., 2000, “Obidicote Project—Word Package 4: Steady-State Test Cases,” Tech. Rep. DNS62433, Rolls-Royce.
Kamboukos, P., Mathioudakis, K., and Stamatis, A., 2002, “Turbofan Performance Deterioration Tracking Using Non-Linear Models and Optimization Techniques,” ASME J. Turbomach., 124, pp. 580–587.

## Figures

Fig. 2

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

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].

Fig. 3

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

Fig. 4

The turbofan layout used as an application test case

## Errata

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