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Communication Dans Un Congrès Année : 2007

Time-Frequency Classification Applied to Induction Machine Faults Monitoring

Résumé

This paper presents a new method of designing an optimized time-frequency representation (TFR) from a time-frequency ambiguity plane applied to the induction machine faults classification. This method is composed of two sequential processes: a feature extraction and a classification. In the process features extraction, the time-frequency representation (TFR) have been designed for maximizing the separability between classes representing the different faults; bearing fault, stator fault and rotor fault. The classification of a new signal is based on the Mahalanobis distance. The diagnosis is independent from the level of load. This method is validated on an 5.5 kW asynchronous motor test bench
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Dates et versions

hal-00399068 , version 1 (25-06-2009)

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Abdesselam Lebaroud, Guy Clerc. Time-Frequency Classification Applied to Induction Machine Faults Monitoring. 32nd IEEE IECON, Nov 2006, Paris, France. pp.5051 - 5056, ⟨10.1109/IECON.2006.347481⟩. ⟨hal-00399068⟩
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