Classification of induction machine faults by K-nearest neighbor

New diagnosis method of induction motor faults based on classification of the current waveforms is presented in this paper. This method is composed of two sequential processes: a feature extraction and a rule decision. The diagnosis is realized the detection of different faults - bearing fault, stat...

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Bibliographic Details
Published in2011 7th International Conference on Electrical and Electronics Engineering (ELECO) pp. I-363 - I-366
Main Authors Bouguerne, A., Lebaroud, A., Medoued, A., Boukadoum, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2011
Subjects
Euclidean distance
Feature extraction
Gravity
Kernel
Stators
Support vector machine classification
Vectors
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Summary:New diagnosis method of induction motor faults based on classification of the current waveforms is presented in this paper. This method is composed of two sequential processes: a feature extraction and a rule decision. The diagnosis is realized the detection of different faults - bearing fault, stator fault and rotor fault. K-nearest neighbor (K-NN) is used as decision criterion. The flexibility of this method allows an accurate classification independent from the level of load. This method is validated on a 5.5-kW induction motor test bench.
ISBN:1467301604
9781467301602