Fault diagnosis in power plant using neural networks

Fault diagnosis and identification (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 difficulties to perform Neural Network training on dynamic patterns, the second approach seems more adequate. In this paper, the FDI methodology consist of two stages. In the first stage, the fault is detected on the basis of residuals generated from a bank of Kalman filters, while, in the second stage, fault identification is obtained from pattern recognition techinques 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.