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FAULT DIAGNOSIS AND AUTOMATIC EXTRACTION OF FUZZY RULES IN AC MOTORS
E. Moya, G.I. Sainz, J. Juez, J. Candau and J. R. Perán
Department of Systems Engineering and Control. School of Industrial
Engineering. University of Valladolid.
Paseo del Cauce s/n, 47011 Valladolid, Spain.
E-mail: {edumoy,gresai,pepcan,peran}@eis.uva.es.
Phone: +34 83 423000 Ext. 4401 Fax: +34 83 423358
In this paper a new approach to fault diagnosis in an AC motor is introduced. This system combines a neuro-fuzzy system called FasArt (Fuzzy Adaptive System ART based) and the well-known fuzzy k nearest neighbor algorithm.
A set of 15 types of non destructive faults has been tested, reaching a high degree of early fault detection and fault type recognition. Moreover, taking into account the neuro-fuzzy nature of the FasArt model, a set of fuzzy rules, containing the knowledge learnt by the system, has been extracted. These rules permit a transfer of the knowledge from a numerical to a symbolic level where the fuzzy rules describe the fault in linguistic terms that can be interpreted by humans in an easier way.
Keywords: Neuro-fuzzy system, fuzzy rules, fault diagnosis, motor
Session slot T-Tu-E04: Fuzzy logic and systems/Area code 3e : Fuzzy and Neural Systems
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