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.
Identification and model-based fault diagnosis of a gas turbine system
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.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.