Deep learning-based research on fault warning for marine dual fuel engines
Dual fuel engines are crucial for ensuring the safe navigation of ships. Predicting the working status of these engines can provide advanced knowledge of their condition and thereby guarantee safe navigation. In this study, a novel deep learning model, the CNN-BiLSTM-KAN, was designed to forecast ex...
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering and Naval Architecture
2025-01-01
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| Series: | Brodogradnja |
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| Online Access: | https://hrcak.srce.hr/file/480767 |
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| author | Lingkai Meng Huibing Gan Haisheng Liu Daoyi Lu |
| author_facet | Lingkai Meng Huibing Gan Haisheng Liu Daoyi Lu |
| author_sort | Lingkai Meng |
| collection | DOAJ |
| description | Dual fuel engines are crucial for ensuring the safe navigation of ships. Predicting the working status of these engines can provide advanced knowledge of their condition and thereby guarantee safe navigation. In this study, a novel deep learning model, the CNN-BiLSTM-KAN, was designed to forecast exhaust gas temperature (EGT) in dual fuel engines operating in gas mode. The model integrated convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and Kolmogorov-Arnold networks (KAN) to perform feature extraction from multi-dimensional time series data, autonomously identify temporal patterns within the data, and directly learn parameterized nonlinear activation functions, respectively. The results reveal that the model obtained a mean square error (MSE) of 0.000051, a root mean square error (RMSE) of 0.007135, a mean absolute error (MAE) of 0.003185, and a mean absolute percentage error (MAPE) of 0.000386. The proposed model demonstrated higher accuracy compared to other forecasting models. Additionally, residual value distribution curves and statistical process control methods were employed to set alarm thresholds for residuals. A sliding window approach was used to establish the alarm threshold for residual standard deviation, with an upper boundary of the residual threshold set at 0.15 and a lower boundary at -0.1. The upper boundary of the residual standard deviation was set at 0.343. Furthermore, the model was validated through a fault dataset. The findings suggest that this approach effectively achieved fault warnings for marine dual-fuel engines. This research provides new references for studies on fault prediction and health management of dual-fuel engines for ships. |
| format | Article |
| id | doaj-art-b559dfb6dac94980b59f4bf0735cb108 |
| institution | Kabale University |
| issn | 0007-215X 1845-5859 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Faculty of Mechanical Engineering and Naval Architecture |
| record_format | Article |
| series | Brodogradnja |
| spelling | doaj-art-b559dfb6dac94980b59f4bf0735cb1082025-08-20T03:31:06ZengFaculty of Mechanical Engineering and Naval ArchitectureBrodogradnja0007-215X1845-58592025-01-0176312810.21278/brod76303Deep learning-based research on fault warning for marine dual fuel enginesLingkai Meng0Huibing Gan1Haisheng Liu2Daoyi Lu3College of Marine Engineering, Dalian Maritime UniversityCollege of Marine Engineering, Dalian Maritime UniversityCollege of Marine Engineering, Dalian Maritime UniversityCollege of Marine Engineering, Dalian Maritime UniversityDual fuel engines are crucial for ensuring the safe navigation of ships. Predicting the working status of these engines can provide advanced knowledge of their condition and thereby guarantee safe navigation. In this study, a novel deep learning model, the CNN-BiLSTM-KAN, was designed to forecast exhaust gas temperature (EGT) in dual fuel engines operating in gas mode. The model integrated convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and Kolmogorov-Arnold networks (KAN) to perform feature extraction from multi-dimensional time series data, autonomously identify temporal patterns within the data, and directly learn parameterized nonlinear activation functions, respectively. The results reveal that the model obtained a mean square error (MSE) of 0.000051, a root mean square error (RMSE) of 0.007135, a mean absolute error (MAE) of 0.003185, and a mean absolute percentage error (MAPE) of 0.000386. The proposed model demonstrated higher accuracy compared to other forecasting models. Additionally, residual value distribution curves and statistical process control methods were employed to set alarm thresholds for residuals. A sliding window approach was used to establish the alarm threshold for residual standard deviation, with an upper boundary of the residual threshold set at 0.15 and a lower boundary at -0.1. The upper boundary of the residual standard deviation was set at 0.343. Furthermore, the model was validated through a fault dataset. The findings suggest that this approach effectively achieved fault warnings for marine dual-fuel engines. This research provides new references for studies on fault prediction and health management of dual-fuel engines for ships.https://hrcak.srce.hr/file/480767marine dual fuel enginecnnbilstmkanfault warning |
| spellingShingle | Lingkai Meng Huibing Gan Haisheng Liu Daoyi Lu Deep learning-based research on fault warning for marine dual fuel engines Brodogradnja marine dual fuel engine cnn bilstm kan fault warning |
| title | Deep learning-based research on fault warning for marine dual fuel engines |
| title_full | Deep learning-based research on fault warning for marine dual fuel engines |
| title_fullStr | Deep learning-based research on fault warning for marine dual fuel engines |
| title_full_unstemmed | Deep learning-based research on fault warning for marine dual fuel engines |
| title_short | Deep learning-based research on fault warning for marine dual fuel engines |
| title_sort | deep learning based research on fault warning for marine dual fuel engines |
| topic | marine dual fuel engine cnn bilstm kan fault warning |
| url | https://hrcak.srce.hr/file/480767 |
| work_keys_str_mv | AT lingkaimeng deeplearningbasedresearchonfaultwarningformarinedualfuelengines AT huibinggan deeplearningbasedresearchonfaultwarningformarinedualfuelengines AT haishengliu deeplearningbasedresearchonfaultwarningformarinedualfuelengines AT daoyilu deeplearningbasedresearchonfaultwarningformarinedualfuelengines |