Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM
To ensure the implementation of the marine electric propulsion self-healing strategy after faults, it is necessary to diagnose and accurately classify the faults. Considering the characteristics of the residual network (ResNet) and bidirectional long short-term memory (BiLSTM), the Res-BiLSTM deep l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/9/736 |
| _version_ | 1797485647226732544 |
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| author | Jialing Xie Weifeng Shi Yuqi Shi |
| author_facet | Jialing Xie Weifeng Shi Yuqi Shi |
| author_sort | Jialing Xie |
| collection | DOAJ |
| description | To ensure the implementation of the marine electric propulsion self-healing strategy after faults, it is necessary to diagnose and accurately classify the faults. Considering the characteristics of the residual network (ResNet) and bidirectional long short-term memory (BiLSTM), the Res-BiLSTM deep learning algorithm is used to establish a fault diagnosis model to distinguish the types of electric drive faults. First, the powerful fault feature extraction ability of the residual network is used to deeply mine the fault features in the signals. Then, perform time-series learning through a bidirectional long short-term memory network, and further excavate the transient time-series features in the fault features so as to achieve the accurate classification of drive inverter faults. The effectiveness of the method is verified using noise-free fault data, and the robustness of the method is verified using data with varying degrees of noise. The results show that compared with conventional deep learning algorithms, Res-BiLSTM has the fastest and most stable training process, the diagnostic performance is improved, and the accuracy can be maintained over 95% under 25–19 dB. It has certain robustness and can be applied to marine electric propulsion systems drive inverter fault diagnosis, and its results can provide data support for the implementation of self-healing control strategies. |
| first_indexed | 2024-03-09T23:21:42Z |
| format | Article |
| id | doaj.art-bc82897f87df47d38200b1a73279c989 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T23:21:42Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-bc82897f87df47d38200b1a73279c9892023-11-23T17:25:59ZengMDPI AGMachines2075-17022022-08-0110973610.3390/machines10090736Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTMJialing Xie0Weifeng Shi1Yuqi Shi2Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Electrical Automation, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Electrical Automation, Shanghai Maritime University, Shanghai 201306, ChinaTo ensure the implementation of the marine electric propulsion self-healing strategy after faults, it is necessary to diagnose and accurately classify the faults. Considering the characteristics of the residual network (ResNet) and bidirectional long short-term memory (BiLSTM), the Res-BiLSTM deep learning algorithm is used to establish a fault diagnosis model to distinguish the types of electric drive faults. First, the powerful fault feature extraction ability of the residual network is used to deeply mine the fault features in the signals. Then, perform time-series learning through a bidirectional long short-term memory network, and further excavate the transient time-series features in the fault features so as to achieve the accurate classification of drive inverter faults. The effectiveness of the method is verified using noise-free fault data, and the robustness of the method is verified using data with varying degrees of noise. The results show that compared with conventional deep learning algorithms, Res-BiLSTM has the fastest and most stable training process, the diagnostic performance is improved, and the accuracy can be maintained over 95% under 25–19 dB. It has certain robustness and can be applied to marine electric propulsion systems drive inverter fault diagnosis, and its results can provide data support for the implementation of self-healing control strategies.https://www.mdpi.com/2075-1702/10/9/736marine electric propulsion systemsix-phase motor drive inverterintelligent fault diagnosisresidual network (ResNet)bidirectional long short-term memory (BiLSTM) |
| spellingShingle | Jialing Xie Weifeng Shi Yuqi Shi Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM Machines marine electric propulsion system six-phase motor drive inverter intelligent fault diagnosis residual network (ResNet) bidirectional long short-term memory (BiLSTM) |
| title | Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM |
| title_full | Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM |
| title_fullStr | Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM |
| title_full_unstemmed | Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM |
| title_short | Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM |
| title_sort | research on fault diagnosis of six phase propulsion motor drive inverter for marine electric propulsion system based on res bilstm |
| topic | marine electric propulsion system six-phase motor drive inverter intelligent fault diagnosis residual network (ResNet) bidirectional long short-term memory (BiLSTM) |
| url | https://www.mdpi.com/2075-1702/10/9/736 |
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