Early Detection and Diagnosis of Faults in an AC Motor Using Neuro Fuzzy Techniques: FasArt + Fuzzy k Nearest Neighbors

Authors:
Javier Juez Santamaría;Gregorio Ismael Sainz Palmero;Eduardo Julio Moya de la Torre;Jose Ramón Perán González
Affiliations:
-;-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

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Abstract

An approach to detect and to classify incipient faults in an AC motor is introduced in this paper. This approach is based in an ART based neuro fuzzy system, (FasArt Fuzzy Adaptive System ART based), and in the fuzzy k nearest neighbor algorithm that is employing in an auxiliary way to complete the learning set.A set of 15 non destructive faults has been tested and both a high degree of early detection and recognition has been reached. As well as, using the neuro-fuzzy nature of the FasArt model a database of fuzzy rules has been obtained permitting a fault description by linguistics terms.