Fault diagnosis and identi®cation (FDI) have been widely developed during recent years. Model-based methods, fault tree approaches and pattern recognition techniques are among the most common methodologies used in such tasks. Neural networks have been used in FDI problems for model approximation and pattern recognition as well. However, because of di culties to perform Neural Network training on dynamic patterns, the second approach seems more adequate. In this paper, the FDI methodology consists of two stages. In the ®rst stage, the fault is detected on the basis of residuals generated from a bank of Kalman ®lters, while, in the second stage, fault identi®cation is obtained from pattern recognition techniques implemented by Neural Networks. The proposed fault diagnosis tool has been tested on a model of a power plant and results from simulations are reported and commented in the paper.

Fault diagnosis in power plant using neural networks

SIMANI, Silvio;FANTUZZI, Cesare
2000

Abstract

Fault diagnosis and identi®cation (FDI) have been widely developed during recent years. Model-based methods, fault tree approaches and pattern recognition techniques are among the most common methodologies used in such tasks. Neural networks have been used in FDI problems for model approximation and pattern recognition as well. However, because of di culties to perform Neural Network training on dynamic patterns, the second approach seems more adequate. In this paper, the FDI methodology consists of two stages. In the ®rst stage, the fault is detected on the basis of residuals generated from a bank of Kalman ®lters, while, in the second stage, fault identi®cation is obtained from pattern recognition techniques implemented by Neural Networks. The proposed fault diagnosis tool has been tested on a model of a power plant and results from simulations are reported and commented in the paper.
Simani, Silvio; Fantuzzi, Cesare
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11392/1209396
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