Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910606947254272 |
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| author | Ngo, Quang-Ha Barnola, Isabel Vu, Tuyen Zhang, Jianhua Ravindra, Harsha Schoder, Karl Ginn, Herbert |
| author_facet | Ngo, Quang-Ha Barnola, Isabel Vu, Tuyen Zhang, Jianhua Ravindra, Harsha Schoder, Karl Ginn, Herbert |
| contents | The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenges strain traditional fault diagnostic methods, making it difficult to detect and manage faults across multiple locations while maintaining system stability and performance. This paper proposes a temporal recurrent graph transformer network for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural network uses gated recurrent units to capture temporal features and a multi-head attention mechanism to extract spatial features, enhancing diagnostic accuracy. The approach effectively identifies and evaluates successive multiple faults with high precision. The method is implemented and validated on the MVDC 12kV shipboard system designed by the ESDRC team, incorporating all key components. Results show significant improvements in fault localization accuracy, with a 1-4% increase in performance metrics compared to other machine learning methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems Ngo, Quang-Ha Barnola, Isabel Vu, Tuyen Zhang, Jianhua Ravindra, Harsha Schoder, Karl Ginn, Herbert Systems and Control The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenges strain traditional fault diagnostic methods, making it difficult to detect and manage faults across multiple locations while maintaining system stability and performance. This paper proposes a temporal recurrent graph transformer network for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural network uses gated recurrent units to capture temporal features and a multi-head attention mechanism to extract spatial features, enhancing diagnostic accuracy. The approach effectively identifies and evaluates successive multiple faults with high precision. The method is implemented and validated on the MVDC 12kV shipboard system designed by the ESDRC team, incorporating all key components. Results show significant improvements in fault localization accuracy, with a 1-4% increase in performance metrics compared to other machine learning methods. |
| title | Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2409.10792 |