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




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