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In this work we propose an original fault signature based on the Hilbert-Park Lissajou’s curve analysis. The performances of the proposed signature were compared to those of the Park Lissajou’s curve which is the signature most recently used. ¶The proposed fault signature does not require a long temporal recording, and their processing is simple. This analysis offers an easy interpretation to conclude on the induction motor condition and its voltage supply state. The proposed signature shows its efficiency especially in the case of unloaded machine. The geometrical characteristic of all Hilbert-Park Lissajou’s curves are calculated  in order to develop the input vector necessary for the pattern recognition tools based on neural network approach with an aim of classifying automatically the various states of the induction motor. This approach was applied to a 1.1 kw induction motor under normal operation and with the following faults: unbalanced voltage, air-gap eccentricity and outer raceway bearing defect

Induction Motor Fault Diagnosis Using a Hilbert-Park Lissajou’s Curve Analysis and Neural Network-Based Decision