Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks
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 paramet...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
|
| Series: | Power Electronics and Drives |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/pead-2024-0002 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850194548773355520 |
|---|---|
| author | Skowron Maciej |
| author_facet | Skowron Maciej |
| author_sort | Skowron Maciej |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b5eb0aff79ce439d9bb9a769e3e601dd |
| institution | OA Journals |
| issn | 2543-4292 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Power Electronics and Drives |
| spelling | doaj-art-b5eb0aff79ce439d9bb9a769e3e601dd2025-08-20T02:13:58ZengSciendoPower Electronics and Drives2543-42922024-01-0191213310.2478/pead-2024-0002Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional NetworksSkowron Maciej01Department of Electrical Machines, Drives and Measurements, Wrocław University of Science and Technology, Wrocław, PolandModern 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.https://doi.org/10.2478/pead-2024-0002transfer learningmotor fault detectioninter-turn short circuitsconvolutional neural networkfield-circuit pmsm model |
| spellingShingle | Skowron Maciej Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks Power Electronics and Drives transfer learning motor fault detection inter-turn short circuits convolutional neural network field-circuit pmsm model |
| title | Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks |
| title_full | Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks |
| title_fullStr | Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks |
| title_full_unstemmed | Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks |
| title_short | Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks |
| title_sort | analysis of pmsm short circuit detection systems using transfer learning of deep convolutional networks |
| topic | transfer learning motor fault detection inter-turn short circuits convolutional neural network field-circuit pmsm model |
| url | https://doi.org/10.2478/pead-2024-0002 |
| work_keys_str_mv | AT skowronmaciej analysisofpmsmshortcircuitdetectionsystemsusingtransferlearningofdeepconvolutionalnetworks |