A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power Plant Data

The surveillance and control of any industrial plant is based on the readings of a set of sensors. Their reliable operation is essential since the output of the sensors provide the only objective information about the state of the process. The signal validation task is intended to confirm whether the sensors are functioning properly Real-time process signal validation is an application field where the use of fuzzy logic and artificial neural networks (ANNs) can improve the diagnosis of faulty sensors or drift in sensor readings in a robust and reliable way The present work describes the transient and steady state on-line validation method of plant process signals using ANN and fuzzy logic pattern recognition. This method has been developed at the OECD Halden Reactor Project and tested on simulated scenarios covering the whole range of Pressurized Water Reactors (PWR) operational conditions provided by Electricite' De France (EDF) and the Centre D'Etudes De Cadarache (CEA)in France.