Dynamic pattern recognition for the diagnosis of evolving systems

In this paper, we propose an approach to achieve the monitoring of the functioning (normal, faulty) of the Steam Generator (SG) of the nuclear Prototype Fast Reactor (PFR). This approach is based on three steps: signal analysis, clustering and classification. The first step analyzes the acoustic signals measuring the noises issued of the injection of water or Argon in the SG. These injections simulate a leakage representing a faulty functioning mode of the steam generator. The goal of the signal analysis is to determine the minimal set of parameters required to discriminate the normal and faulty modes in the feature space. In the clustering step, the patterns obtained by the acoustic signals analysis are labeled as belonging to the first class (non-injection) or to the second class (injection) corresponding respectively to normal and faulty functioning modes. Finally, the decision function is generated in the third step in order to assign a new pattern (new acoustic signal) to one of the two learned classes. We use the Semi-Supervised Dynamic Fuzzy K-Nearest Neighbours (SS-DFKNN) method to achieve the clustering and the online classification of the new incoming patterns.