Data-driven time-frequency classification techniques applied to tool-wear monitoring
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Optimizing time-frequency kernels for classification
IEEE Transactions on Signal Processing
Artificial immune classifier with swarm learning
Engineering Applications of Artificial Intelligence
Planning for mechatronics systems-Architecture, methods and case study
Engineering Applications of Artificial Intelligence
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This paper presents a new diagnosis method of induction motor faults based on time-frequency classification of the current waveforms. This method is composed of two sequential processes: a feature extraction and a rule decision. In the process of feature extraction, the time-frequency representation (TFR) has been designed for maximizing the separability between classes representing different faults. The diagnosis is realised in two levels; the first one allows the detection of different faults-bearing fault, stator fault and rotor fault. The second one refines this detection by the determination of severity degree of faults, which are already identified on the previous level. The diagnosis is independent of the level of load. This method is validated on a 5.5kW induction motor test bench.