A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed
Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction ca...
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/11/2046 |
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| author | Ji Gan Huabiao Jin Qianming Shang Chenxing Sheng |
| author_facet | Ji Gan Huabiao Jin Qianming Shang Chenxing Sheng |
| author_sort | Ji Gan |
| collection | DOAJ |
| description | Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations. |
| format | Article |
| id | doaj-art-835bf6e815784284b46ffc73e41d397b |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-835bf6e815784284b46ffc73e41d397b2024-11-26T18:08:24ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011211204610.3390/jmse12112046A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational SpeedJi Gan0Huabiao Jin1Qianming Shang2Chenxing Sheng3School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaShip engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.https://www.mdpi.com/2077-1312/12/11/2046ship dual-fuel engineinternal combustion engines (ICEs)instantaneous rotational speedmisfire fault diagnosisanomaly detectionmachine learning |
| spellingShingle | Ji Gan Huabiao Jin Qianming Shang Chenxing Sheng A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed Journal of Marine Science and Engineering ship dual-fuel engine internal combustion engines (ICEs) instantaneous rotational speed misfire fault diagnosis anomaly detection machine learning |
| title | A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed |
| title_full | A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed |
| title_fullStr | A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed |
| title_full_unstemmed | A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed |
| title_short | A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed |
| title_sort | new method of intelligent fault diagnosis of ship dual fuel engine based on instantaneous rotational speed |
| topic | ship dual-fuel engine internal combustion engines (ICEs) instantaneous rotational speed misfire fault diagnosis anomaly detection machine learning |
| url | https://www.mdpi.com/2077-1312/12/11/2046 |
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