Classification of Induction Machine Faults by Optimal Time-Frequency Representations

Abdesselam Lebaroud (1) , Guy Clerc (2)

1 LEC - Laboratoire d'Electrotechnique de Constantine
2 AMPERE - Ampère

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.

Mots clés: Diagnosis; hidden Markov model (HMM); induction motor; time–frequency classification

Domaines: Energie électrique

Abdesselam Lebaroud, Guy Clerc. Classification of Induction Machine Faults by Optimal Time-Frequency Representations. IEEE Transactions on Industrial Electronics, 2008, 55 (12), pp.4290 - 4298. ⟨10.1109/TIE.2008.2004666⟩. ⟨hal-00368719⟩