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Classification of Induction Machine Faults by Optimal Time–Frequency Representations

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2 Author(s)
Abdesselam Lebaroud ; Lab. LEC, Constantine Univ., Constantine ; Guy Clerc

This paper presents a new diagnosis method of induction motor faults based on time-frequency classification of the current waveforms. This method is based on a representation space, a selection criterion, and a decision criterion. In order to define the representation space, an optimized time-frequency representation (TFR) is designed from the time-frequency ambiguity plane. The selection criterion is based on Fisher's discriminant ratio, which allows one to maximize the separability between classes representing different faults. A distinct TFR is designed for each class. The following two classifiers were used for decision criteria: the Mahalanobis distance and the hidden Markov model. 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.

Published in:

IEEE Transactions on Industrial Electronics  (Volume:55 ,  Issue: 12 )