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Classification of Induction Machine Faults using Time Frequency Representation and Particle Swarm Optimization
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 Title & Authors
Classification of Induction Machine Faults using Time Frequency Representation and Particle Swarm Optimization
Medoued, A.; Lebaroud, A.; Laifa, A.; Sayad, D.;
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 Abstract
This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.
 Keywords
Induction machine diagnosis;Ambiguity plane;Classification-optimal TFR;Time-frequency;Fisher's discriminated ratio;Artificial neural network ANN;PSO;
 Language
English
 Cited by
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A Fault Severity Index for Stator Winding Faults Detection in Vector Controlled PM Synchronous Motor, Journal of Electrical Engineering and Technology, 2015, 10, 6, 2326  crossref(new windwow)
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