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. © Springer-Verlag Berlin Heidelberg 2001.
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CITATION STYLE
Juez, J., Sainz, G. I., Moya, E. J., & Perán, J. R. (2001). Early detection and diagnosis of faults in an AC motor using neuro fuzzy techniques: FasArt + Fuzzy K nearest neighbors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2085 LNCS, pp. 571–578). Springer Verlag. https://doi.org/10.1007/3-540-45723-2_69
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