@ARTICLE{Skowron_M._Stator_2020,
 author={Skowron, M. and Wolkiewicz, M. and Tarchała, G.},
 volume={68},
 number={No. 5 (i.a. Special Section on Modern control of drives and power converters)},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={1039-1048},
 howpublished={online},
 year={2020},
 abstract={In this paper deep neural networks are proposed to diagnose inter-turn short-circuits of induction motor stator windings operating under the Direct Field Oriented Control method. A convolutional neural network (CNN), trained with a Stochastic Gradient Descent with Momentum method is used. This kind of deep-trained neural network allows to significantly accelerate the diagnostic process compared to the traditional methods based on the Fast Fourier Transform as well as it does not require stationary operating conditions. To assess the effectiveness of the applied CNN-based detectors, the tests were carried out for variable load conditions and different values of the supply voltage frequency. Experimental results of the proposed induction motor fault detection system are presented and discussed.},
 type={Article},
 title={Stator winding fault diagnosis of induction motor operating under the field-oriented control with convolutional neural networks},
 URL={http://journals.pan.pl/Content/117693/PDF/09_D1039-1048_01553_Bpast.No.68-5_30.10.20_.pdf},
 doi={10.24425/bpasts.2020.134660},
 keywords={diagnostics, stator faults, field-oriented control, convolutional neural networks},
}