Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 54, Issue 1, Pages 259-264Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2006.888790
Keywords
fault diagnosis; Hebbian learning; induction motors; neural networks; unsupervised learning
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In this paper, an automatic algorithm based an unsupervised neural network for an on-line diagnostics of three-phase induction motor stator fault is presented. This algorithm uses the alfa-beta stator currents, as input variables. Then, a fully automatic unsupervised method is applied in which a Hebbian-based unsupervised neural network is used to extract the principal components of the stator current data. These main directions are used to decide where the fault occurs and a relationship between the current components is calculated to verify the severity of the fault. One of the characteristics of this method, given its unsupervised nature, is that it does not need a prior identification of the system. The proposed methodology has been experimentally tested on a 1 kW induction motor. The obtained experimental results show the effectiveness of the proposed method.
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