<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 focussed on a selected type of damage or motor type with a limited range of rated parameters. The application of the idea of transfer learning (TL) allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. In the research, the possibility of using the TL 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 TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), 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 a weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.</p>
</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 focussed on a selected type of damage or motor type with a limited range of rated parameters. The application of the idea of transfer learning (TL) allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. In the research, the possibility of using the TL 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 TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), 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 a weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks

Department of Electrical Machines, Drives and Measurements

Wrocław University of Science and Technology

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modern permanent magnet synchronous motor (PMSM) diagnostic systems are now combined with advanced artificial intelligence techniques, such as deep neural...

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks

Data publikacji: 20 sty 2024

Zakres stron: 21 - 33

Otrzymano: 29 paź 2023

Przyjęty: 08 gru 2023

DOI: https://doi.org/10.2478/pead-2024-0002

Słowa kluczowe
transfer learning, motor fault detection, inter-turn short circuits, convolutional neural network, field-circuit PMSM model

© 2024 Maciej Skowron, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks

Data publikacji: 20 sty 2024

Zakres stron: 21 - 33

Otrzymano: 29 paź 2023

Przyjęty: 08 gru 2023

DOI: https://doi.org/10.2478/pead-2024-0002

Słowa kluczowetransfer learning, motor fault detection, inter-turn short circuits, convolutional neural network, field-circuit PMSM model

© 2024 Maciej Skowron, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
transfer learning, motor fault detection, inter-turn short circuits, convolutional neural network, field-circuit PMSM model