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 recogn ition techniques are among the most common methodologies used in such tasks . Neural networks have been used in FDI problems for model approximation an d pattern recognition as well. However, because of difficulties 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 th e first stage, the fault is detected on the basis of residuals generated fr om a bank of Kalman filters, while, in the second stage, fault identificati on is obtained from pattern recognition techniques implemented by Neural Ne tworks. The proposed fault diagnosis tool has been tested on a model of a p ower plant acid results from simulations are reported and commented in the paper. (C) 2000 Elsevier Science Inc. All rights reserved.