This paper presents a fuzzy models bank to detect and to identify faults using the multimodel technique, calculating a non-linear fuzzy model for each operation mode of the system. A comparison amongst the output of each model with the actual plant data isolates the faults, i.e., the operation mode of the system (normal or faulty one). Each of the considered fuzzy models is defined by a set of fuzzy rules that explain the system behaviour. These fuzzy models obtained from experimental data can be improved, through the fuzzy rules, in order to use all the characteristics of the fuzzy logic in terms of linguistic capacity (linguistic modelling). The fuzzy models are improved using similarity measurements, reducing the number of rules, eliminating incoherencies, redundancies and increasing their interpretability capacity. This method has been applied to an induction motor, in order to illustrate its behaviour and its applicability. The results shown that this method is able to detect and to identify faults even after the simplification of the models. Copyright © 2007 CEA-IFAC.
Fuente, M. J., Moya, E., & Palmero, G. I. S. (2007). Esquema de detección de fallos difuso basado en modelado lingüístico-preciso de un motor de inducción. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, 4(2). https://doi.org/10.1016/s1697-7912(07)70211-x