Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics
نویسندگان
چکیده
Self-Organizing Maps (SOM) is an excellent method of analyzing multidimensional data. The SOM based classification is attractive, due to its unsupervised learning and topology preserving properties. In this paper, the performance of the self-organizing methods is investigated in induction motor rotor fault detection and severity evaluation. The SOM is based on motor current signature analysis (MCSA). The agglomerative hierarchical algorithms using the Ward’s method is applied to automatically dividing the map into interesting interpretable groups of map units that correspond to clusters in the input data. The results obtained with this approach make it possible to detect a rotor bar fault just directly from the visualization results. The system is also able to estimate the extent of rotor faults.
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