Classification of Induction Machine Faults by Optimal Time-Frequency Representations

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.