Analysis of PMSM short-circuit detection systems using transfer learning of deep convolutional networks
 
 
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Wroclaw University of Science and Technology
 
 
Corresponding author
Maciej Skowron   

Wroclaw University of Science and Technology
 
 
Power Electronics and Drives 2024;9 (44)
 
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ABSTRACT
Modern permanent magnet synchronous motor (PMSM) diagnostic systems are now combined with advanced artificial intelligence techniques, such as deep neural networks. However, the design of such systems is mainly focused on a selected type of damage or motor type with a limited range of parameters. The application of transfer learning allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. In research, the possibility of using the transfer learning idea in the implementation of PMSM stator windings fault detection systems was considered. The method is based on the characteristic symptoms of stator defects determined for another type of motor or mathematical model in the target diagnostic application of PMSM. This paper presents a comparison of PMSM motor inter-turn short circuit fault detection systems using transfer learning of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network, it was possible to ensure high accuracy of fault detection with simultaneously short reaction time to occurring fault. The technique used was based on the use of weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.
eISSN:2543-4292
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