Modelling, identification and fault diagnosis of a simulated model of an industrial gas turbine

The objective of this paper is to model, identify, and 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. An ARX model is 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 model.

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