KEYNOTE PAPER - FAULT-DIAGNOSIS AND NEURAL NETWORKS - A POWER-PLANT APPLICATION

Correct and timely fault detection is of major importance in the field of system engineering, and constitutes a primary problem in a broad s pectrum of cases, from industrial processes to high-performance system s and to mass-produced consumer equipment. A large number of methods c an be found in the literature, and the recent use of neural networks f or solving fault-diagnosis problems in real industrial situations seem s to be particularly promising. This paper describes a neural approach to solving approximately some very difficult fault-diagnosis problems . A real system (the four heaters of a feedwater high-pressure line of a 320 MW power plant) has been chosen to test the neural methodology. Simulation results obtained by a very accurate and validated model of the plant show the effectiveness of using multilayer feedforward and Radial Basis Functions neural networks to solve real fault-detection a nd diagnosis problems.