Fuzzy classification using ART2 networks for a non-linear actuator

Nowadays classical strategies for fault classification present the drawback of non-identification of new fault scenarios on-line. Therefore, its classification on-line 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 on-line 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.