Classification of Induction Machine Faults by Optimal Time–Frequency Representations
Lebaroud, A.
Clerc, G.
Lab. LEC, Constantine Univ., Constantine;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec. 2008
Volume: 55,
Issue: 12
On page(s): 4290-4298
ISSN: 0278-0046
INSPEC Accession Number: 10348769
Digital Object Identifier: 10.1109/TIE.2008.2004666
First Published: 2008-09-12
Current Version Published: 2008-12-02
Abstract
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.
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