Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis of inter-turn...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/4064 |
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| author | Fengxin Ma Liang Qi Shuxia Ye Yuting Chen Han Xiao Shankai Li |
| author_facet | Fengxin Ma Liang Qi Shuxia Ye Yuting Chen Han Xiao Shankai Li |
| author_sort | Fengxin Ma |
| collection | DOAJ |
| description | The permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis of inter-turn short circuits, this paper proposes an intelligent fault diagnosis method based on improved variational mode decomposition (VMD), multi-scale principal component analysis (PCA) feature extraction, and improved Bi-LSTM. Firstly, the stator current simulation dataset is obtained by using the mathematic model of the inter-turn short-circuit of PMSM, and the parameters of VMD are optimized by the grey wolf algorithm. Then, the data is coarse-grained to obtain multi-scale features, and the main features are selected as the sample data for fault classification by PCA. Subsequently, the Bi-LSTM neural network is used for training and analyzing the data of the sample set and the test set. Finally, the learning rate and the number of hidden-layer nodes of the Bi-LSTM are optimized by the whale algorithm to increase the diagnosis accuracy. Experimental results show that the accuracy of the proposed method for inter-turn short-circuited fault diagnosis is as high as 100%, which confirms the effectiveness of the method. |
| first_indexed | 2024-03-11T06:57:31Z |
| format | Article |
| id | doaj.art-700882e0b5a74c8eb443466b71e0a4be |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:57:31Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-700882e0b5a74c8eb443466b71e0a4be2023-11-17T09:30:59ZengMDPI AGApplied Sciences2076-34172023-03-01136406410.3390/app13064064Research on Fault Diagnosis Algorithm of Ship Electric Propulsion MotorFengxin Ma0Liang Qi1Shuxia Ye2Yuting Chen3Han Xiao4Shankai Li5School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaThe permanent magnet synchronous motor (PMSM) has been used in electric propulsion and other fields. However, it is prone to the stator winding inter-turn short-circuit, and if no effective measures are taken, the ship’s power system will be paralyzed. To realize intelligent diagnosis of inter-turn short circuits, this paper proposes an intelligent fault diagnosis method based on improved variational mode decomposition (VMD), multi-scale principal component analysis (PCA) feature extraction, and improved Bi-LSTM. Firstly, the stator current simulation dataset is obtained by using the mathematic model of the inter-turn short-circuit of PMSM, and the parameters of VMD are optimized by the grey wolf algorithm. Then, the data is coarse-grained to obtain multi-scale features, and the main features are selected as the sample data for fault classification by PCA. Subsequently, the Bi-LSTM neural network is used for training and analyzing the data of the sample set and the test set. Finally, the learning rate and the number of hidden-layer nodes of the Bi-LSTM are optimized by the whale algorithm to increase the diagnosis accuracy. Experimental results show that the accuracy of the proposed method for inter-turn short-circuited fault diagnosis is as high as 100%, which confirms the effectiveness of the method.https://www.mdpi.com/2076-3417/13/6/4064fault diagnosispermanent magnet synchronous motorinterturn short-circuitedvariational mode decompositionmulti-scalebidirectional long short-term memory neural network |
| spellingShingle | Fengxin Ma Liang Qi Shuxia Ye Yuting Chen Han Xiao Shankai Li Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor Applied Sciences fault diagnosis permanent magnet synchronous motor interturn short-circuited variational mode decomposition multi-scale bidirectional long short-term memory neural network |
| title | Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor |
| title_full | Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor |
| title_fullStr | Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor |
| title_full_unstemmed | Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor |
| title_short | Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor |
| title_sort | research on fault diagnosis algorithm of ship electric propulsion motor |
| topic | fault diagnosis permanent magnet synchronous motor interturn short-circuited variational mode decomposition multi-scale bidirectional long short-term memory neural network |
| url | https://www.mdpi.com/2076-3417/13/6/4064 |
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