%0 Journal Article
	%A Djalal Eddine Khodja and  Boukhemis Chetate
	%D 2010
	%J International Journal of Mechanical and Mechatronics Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 47, 2010
	%T Torque Based Selection of ANN for Fault Diagnosis of Wound Rotor Asynchronous Motor-Converter Association
	%U https://publications.waset.org/pdf/15872
	%V 47
	%X In this paper, an automatic system of diagnosis was
developed to detect and locate in real time the defects of the wound
rotor asynchronous machine associated to electronic converter. For
this purpose, we have treated the signals of the measured parameters
(current and speed) to use them firstly, as indicating variables of the
machine defects under study and, secondly, as inputs to the Artificial
Neuron Network (ANN) for their classification in order to detect the
defect type in progress. Once a defect is detected, the interpretation
system of information will give the type of the defect and its place of
appearance.
	%P 1314 - 1318