Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data
The inconsistency of battery voltages in all-electric ships is a significant issue for electric vehicle battery systems, leading to numerous safety concerns during vessel operation. Therefore, timely fault diagnosis and accurate fault prediction are crucial for the safe operation of ships. This stud...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2253 |
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| _version_ | 1846104146198921216 |
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| author | Yifan Liu Huabiao Jin Xiangguo Yang Telu Tang Qijia Song Yuelin Chen Lin Liu Shoude Jiang |
| author_facet | Yifan Liu Huabiao Jin Xiangguo Yang Telu Tang Qijia Song Yuelin Chen Lin Liu Shoude Jiang |
| author_sort | Yifan Liu |
| collection | DOAJ |
| description | The inconsistency of battery voltages in all-electric ships is a significant issue for electric vehicle battery systems, leading to numerous safety concerns during vessel operation. Therefore, timely fault diagnosis and accurate fault prediction are crucial for the safe operation of ships. This study examines the fault alarm system of marine battery management systems in conjunction with the unique operating conditions of ships, focusing on the system’s latency. To facilitate prompt fault detection, a fault diagnosis method based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed, utilizing the voltage data of battery clusters. Results indicate that the DBSCAN clustering algorithm demonstrates superior effectiveness and accuracy in identifying irregular battery clusters. Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. Experimental findings suggest that this model can effectively predict consistency faults and over-/under-voltage conditions based on battery cluster voltage values and corresponding fault thresholds. |
| format | Article |
| id | doaj-art-6d8de1d7e0544e2f86603ecfb71b1b4a |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-6d8de1d7e0544e2f86603ecfb71b1b4a2024-12-27T14:33:23ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212225310.3390/jmse12122253Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real DataYifan Liu0Huabiao Jin1Xiangguo Yang2Telu Tang3Qijia Song4Yuelin Chen5Lin Liu6Shoude Jiang7School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, ChinaThe inconsistency of battery voltages in all-electric ships is a significant issue for electric vehicle battery systems, leading to numerous safety concerns during vessel operation. Therefore, timely fault diagnosis and accurate fault prediction are crucial for the safe operation of ships. This study examines the fault alarm system of marine battery management systems in conjunction with the unique operating conditions of ships, focusing on the system’s latency. To facilitate prompt fault detection, a fault diagnosis method based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed, utilizing the voltage data of battery clusters. Results indicate that the DBSCAN clustering algorithm demonstrates superior effectiveness and accuracy in identifying irregular battery clusters. Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. Experimental findings suggest that this model can effectively predict consistency faults and over-/under-voltage conditions based on battery cluster voltage values and corresponding fault thresholds.https://www.mdpi.com/2077-1312/12/12/2253marine lithium-ion batteryfault diagnosisDBSCAN algorithmiTransformer modelreal-world driving data |
| spellingShingle | Yifan Liu Huabiao Jin Xiangguo Yang Telu Tang Qijia Song Yuelin Chen Lin Liu Shoude Jiang Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data Journal of Marine Science and Engineering marine lithium-ion battery fault diagnosis DBSCAN algorithm iTransformer model real-world driving data |
| title | Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data |
| title_full | Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data |
| title_fullStr | Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data |
| title_full_unstemmed | Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data |
| title_short | Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data |
| title_sort | early fault diagnosis and prediction of marine large capacity batteries based on real data |
| topic | marine lithium-ion battery fault diagnosis DBSCAN algorithm iTransformer model real-world driving data |
| url | https://www.mdpi.com/2077-1312/12/12/2253 |
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