Keynote paper: Fault diagnosis and neural networks: A power plant application

Abstract Correct and timely fault detection is of major importance in the field of system engineering, and constitutes a primary problem in a broad spectrum of cases, from industrial processes to high-performance systems and to mass-produced consumer equipment. A large number of methods can be found in the literature, and the recent use of neural networks for solving fault-diagnosis problems in real industrial situations seems 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 and diagnosis problems.

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