In this paper a detailed method for fault detection of an in-core three wires Resistance Temperature
Detectors (RTD) sensor is introduced. The method is mainly based on the dependence of the fuel rod
temperature profile on control rods elevation and coolant flow rate in a given nuclear reactor. For the
implementation, an artificial neural network (ANN) technique has been developed to model the dynamic
behaviour of the considered temperature sensor. In order to have more refined model estimation, ANN
has been combined with additional noise reduction algorithms. The effective denoising work was done
via the discrete wavelet transform (DWT) to remove various kinds of artefacts such as inherent measurement
noise. The principle of the adopted fault detection task is based on the calculation of the
difference between the ANN model estimated temperature and the online being measured temperature
and then compare the deviation with a certain detection threshold to decide the sensor fault. The efficiency
of the method is evaluated first on a simulated case and then on the on-line measurements
obtained from a real plant. Results confirm the capacity of the developed ANN-based model to estimate a
fuel rod temperature with a reasonable accuracy
