In this paper a model-based procedure exploiting analytical redundancy for the detection and isolation of faults of a gas turbine system is presented. The diagnosis scheme is based on the generation of so--called ``residuals'' that are errors between estimated and measured variables of the process. The work is completed under both noise-free (deterministic) and noisy (stochastic) conditions. Residual analysis and statistical tests are used for fault detection and isolation, respectively. The final section shows how the actual size of each fault can be estimated using a multi-layer perceptron neural network used as a non-linear function approximator. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model.

Fault Diagnosis of a Simulated Model of an Industrial Gas Turbine Prototype Using Identification Techniques

SIMANI, Silvio;
2001

Abstract

In this paper a model-based procedure exploiting analytical redundancy for the detection and isolation of faults of a gas turbine system is presented. The diagnosis scheme is based on the generation of so--called ``residuals'' that are errors between estimated and measured variables of the process. The work is completed under both noise-free (deterministic) and noisy (stochastic) conditions. Residual analysis and statistical tests are used for fault detection and isolation, respectively. The final section shows how the actual size of each fault can be estimated using a multi-layer perceptron neural network used as a non-linear function approximator. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model.
2001
model-based procedure; analytical redundancy; fault detection and isolation; gas turbine; residual generators; single-shaft industrial gas turbine model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1195673
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