Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System
This article presents the classification of optoelectronics encoder faults in a permanent magnet synchronous motor (PMSM) drive system. This paper proposes the deep neural networks (DNNs) speed sensor faults classification application in the vector-controlled PMSM drive. This approach to the issue h...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/19/4184 |
| _version_ | 1797575942257770496 |
|---|---|
| author | Kamila Jankowska Mateusz Dybkowski |
| author_facet | Kamila Jankowska Mateusz Dybkowski |
| author_sort | Kamila Jankowska |
| collection | DOAJ |
| description | This article presents the classification of optoelectronics encoder faults in a permanent magnet synchronous motor (PMSM) drive system. This paper proposes the deep neural networks (DNNs) speed sensor faults classification application in the vector-controlled PMSM drive. This approach to the issue has not been discussed in the literature before. This work presents a solution based on early detection with the use of the model reference adaptive system (MRAS) estimator and fault classification based on artificial intelligence. The innovative nature of this work is also due to the simulation of speed sensor damage using the developed optoelectronics encoder model in the Matlab/Simulink environment. This work is focused on simulation studies, which have been supported by experimental results obtained on the MicroLabBox platform. This article compares two structures of deep neural networks in fault detection. The results were also compared with previous experimental studies on the classification of speed sensor failures using shallow neural networks. |
| first_indexed | 2024-03-10T21:45:28Z |
| format | Article |
| id | doaj.art-2987f52fc7e4418e82c829b427e6c2e4 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:45:28Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2987f52fc7e4418e82c829b427e6c2e42023-11-19T14:18:25ZengMDPI AGElectronics2079-92922023-10-011219418410.3390/electronics12194184Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive SystemKamila Jankowska0Mateusz Dybkowski1Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandDepartment of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandThis article presents the classification of optoelectronics encoder faults in a permanent magnet synchronous motor (PMSM) drive system. This paper proposes the deep neural networks (DNNs) speed sensor faults classification application in the vector-controlled PMSM drive. This approach to the issue has not been discussed in the literature before. This work presents a solution based on early detection with the use of the model reference adaptive system (MRAS) estimator and fault classification based on artificial intelligence. The innovative nature of this work is also due to the simulation of speed sensor damage using the developed optoelectronics encoder model in the Matlab/Simulink environment. This work is focused on simulation studies, which have been supported by experimental results obtained on the MicroLabBox platform. This article compares two structures of deep neural networks in fault detection. The results were also compared with previous experimental studies on the classification of speed sensor failures using shallow neural networks.https://www.mdpi.com/2079-9292/12/19/4184DNN2D-CNNspeed sensorFTCPMSM |
| spellingShingle | Kamila Jankowska Mateusz Dybkowski Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System Electronics DNN 2D-CNN speed sensor FTC PMSM |
| title | Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System |
| title_full | Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System |
| title_fullStr | Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System |
| title_full_unstemmed | Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System |
| title_short | Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System |
| title_sort | classification of optoelectronic rotary encoder faults based on deep learning methods in permanent magnet synchronous motor drive system |
| topic | DNN 2D-CNN speed sensor FTC PMSM |
| url | https://www.mdpi.com/2079-9292/12/19/4184 |
| work_keys_str_mv | AT kamilajankowska classificationofoptoelectronicrotaryencoderfaultsbasedondeeplearningmethodsinpermanentmagnetsynchronousmotordrivesystem AT mateuszdybkowski classificationofoptoelectronicrotaryencoderfaultsbasedondeeplearningmethodsinpermanentmagnetsynchronousmotordrivesystem |