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

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...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Yousefi, Iman, Khaloozadeh, Hamid, Ashraf-Modarres, Ali
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Computational modeling
Equations
Fault detection
Fault diagnosis
Gas Turbine
Identification Methods
Mathematical model
Neural Networks
Nonlinear dynamical systems
Turbines
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Beschreibung
Zusammenfassung: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.