Identification and Fault Diagnosis of an Industrial Gas Turbine Using State-Space Methods


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The objective of this paper is to identify, detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called "residuals" that are errors between estimated and measured variables of the process. A State-Space model is used for identification and some observer-based methods are used for residual generation, while for residual evaluation a neural network classifier for MLP is used. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine simulator.



Advanced Materials Research (Volumes 383-390)

Edited by:

Wu Fan




I. Yousefi et al., "Identification and Fault Diagnosis of an Industrial Gas Turbine Using State-Space Methods", Advanced Materials Research, Vols. 383-390, pp. 1000-1006, 2012

Online since:

November 2011




[1] S. Simani, C. Fantuzzi, and S. Beghelli, Diagnosis techniques for sensor faults of industrial processes, IEEE Trans. Control Syst. Technol., vol. 8, no. 5, pp.848-855, Sep. (2000).


[2] S. Simani and C. Fantuzzi, Fault diagnosis in power plant using neural networks, Int. J. Inform. Sci., vol. 127, pp.125-136, Aug. (2000).


[3] S. Simani and C. Fantuzzi, and P. R. Spina, Application of a neural network in gas turbine control sensor fault detection, Int. Conf. on Control Applications, Italy 1-4 Sep. (1998).


[4] R. J. Patton, P. M. Frank and R. N. Clark, Eds., Fault diagnosis in dynamic systems, theory and application, ser. Control Engineering. London, U.K.: Prentice-Hall, (1989).

[5] M. Moonen, B. De Moor, L. Vandenberghe and J. Vandewalle, On- and Off-Line Identification of Linear State-Space Models, Int. J. of Control 49 (1989) 219-232.


[6] R. Isermann, Process fault detection based ob modeling and estimation methods: a survey, Automatica, vol. 20, pp.387-404, Jul. (1984).


[7] S. Simani, Identification and fault diagnosis of a simulated model of an industrial gas turbine, IEEE Trans. Indust. Inform., vol. 1, no. 3, Aug. (2005).

[8] S. Simani and C. Fantuzzi, and R. J. Patton, Model-Based Fault diagnosis in dynamic systems using identification techniques 1st ed. London, U.K.: Springer-Verlag, Nov. (2002).


[9] MathWorks, SIMULINK: User's Guide, The MathWork, Inc., Natick, MA, Feb., (2010).

[10] MathWork, Inc., MATLAB User's Guide, Natick, MA, (2006).

[11] J. Gertler, Fault detection and diagnosis in engineering systems, New York: Marcel Dekker, (1998).

[12] P. M. Frank, Fault diagnosis in dynamic systems using analytical and knowledge based redundancy: a survey of some new results, Automatica, vol. 26, pp.459-474, May (1990).


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