Fuzzy classification using ART2 networks for a non-linear actuator

Classical strategies for fault classification have the drawback that they do not identify new fault scenarios online. Therefore, classification online becomes dependant on computation delays. In here, this problem is taken as a pattern recognition issue. The approach followed is based upon a fuzzy ART2 network. It consists of two modules, firstly the recognition of new scenarios is performed by the network. Secondly, the classification of every group of patterns is performed by a decision-making procedure. This work addresses the problem of fault classification online as a problem of pattern recognition rather than a fault detection approach. The use of pattern recognition presents the advantage of classification of recognized patterns as non fault scenario. The appearance of new patterns is taken as part of fault behaviour