Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network
With the long-term operation of ships, the performance of marine diesel engines gradually declines due to the wear of internal moving components, increasing the risk of potential failures. Fuel consumption is a critical indicator for assessing engine operating conditions, and accurately predicting b...
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1140 |
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| author | Defu Zhang Yuxuan Song Jianfeng Gao Zhenyu Shen Liangkuan Li Anren Yao |
| author_facet | Defu Zhang Yuxuan Song Jianfeng Gao Zhenyu Shen Liangkuan Li Anren Yao |
| author_sort | Defu Zhang |
| collection | DOAJ |
| description | With the long-term operation of ships, the performance of marine diesel engines gradually declines due to the wear of internal moving components, increasing the risk of potential failures. Fuel consumption is a critical indicator for assessing engine operating conditions, and accurately predicting baseline fuel consumption under normal operating conditions is essential for evaluating ship energy efficiency and conducting fault diagnosis. To address common issues in marine engine operational data, such as noise pollution, missing values, inconsistent scales, and feature redundancy, a Diesel Engine Data Enhancement and Optimization Framework (DEOF) was developed to systematically improve data quality. Furthermore, to overcome the limitations of existing models, such as insufficient prediction accuracy and poor stability under complex operating conditions, a Meta-learning Diffusion Residual Attention Network (MD-RAN) is proposed. This approach leverages the strengths of diffusion models in nonlinear generative modeling, integrates meta-learning mechanisms to enhance task adaptation speed, employs multi-head attention modules to strengthen feature extraction, and incorporates dynamic residual connections to improve training stability and flexibility. The data used in this study were collected from real-world operations of ocean-going vessels, ensuring high representativeness. This paper systematically benchmarks the proposed model with the traditional learning model. The results are verified to be effective. The MD-RAN algorithm is significantly better than the original model in terms of prediction accuracy, stability, and nonlinear expression ability. The R<sup>2</sup> value can reach 0.9853, and the RMSE and MAE are as low as 1.5801 and 1.1879, respectively. Its feasibility will be further evaluated in practical applications in the future. This study not only provides a systematic data-driven modeling framework, offering technical insights for constructing high-quality datasets, but also establishes a novel generative modeling approach for marine diesel engine fuel consumption prediction, providing robust support for intelligent engine maintenance and energy efficiency optimization. |
| format | Article |
| id | doaj-art-bf8c63447cdd4ae09802be301476ecde |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-bf8c63447cdd4ae09802be301476ecde2025-08-20T03:27:27ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01136114010.3390/jmse13061140Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative NetworkDefu Zhang0Yuxuan Song1Jianfeng Gao2Zhenyu Shen3Liangkuan Li4Anren Yao5Maritime College, Tianjin University of Technology, Tianjin 300384, ChinaMaritime College, Tianjin University of Technology, Tianjin 300384, ChinaTianjin Aids to Navigation Department of NGCN, Tianjin 300456, ChinaMaritime College, Tianjin University of Technology, Tianjin 300384, ChinaDepartment of Navigation Technology, Tianjin Maritime College, Tianjin 300350, ChinaTianjin Deren Dual-Fuel Environmental Protection Technology Co., Ltd., Tianjin 471599, ChinaWith the long-term operation of ships, the performance of marine diesel engines gradually declines due to the wear of internal moving components, increasing the risk of potential failures. Fuel consumption is a critical indicator for assessing engine operating conditions, and accurately predicting baseline fuel consumption under normal operating conditions is essential for evaluating ship energy efficiency and conducting fault diagnosis. To address common issues in marine engine operational data, such as noise pollution, missing values, inconsistent scales, and feature redundancy, a Diesel Engine Data Enhancement and Optimization Framework (DEOF) was developed to systematically improve data quality. Furthermore, to overcome the limitations of existing models, such as insufficient prediction accuracy and poor stability under complex operating conditions, a Meta-learning Diffusion Residual Attention Network (MD-RAN) is proposed. This approach leverages the strengths of diffusion models in nonlinear generative modeling, integrates meta-learning mechanisms to enhance task adaptation speed, employs multi-head attention modules to strengthen feature extraction, and incorporates dynamic residual connections to improve training stability and flexibility. The data used in this study were collected from real-world operations of ocean-going vessels, ensuring high representativeness. This paper systematically benchmarks the proposed model with the traditional learning model. The results are verified to be effective. The MD-RAN algorithm is significantly better than the original model in terms of prediction accuracy, stability, and nonlinear expression ability. The R<sup>2</sup> value can reach 0.9853, and the RMSE and MAE are as low as 1.5801 and 1.1879, respectively. Its feasibility will be further evaluated in practical applications in the future. This study not only provides a systematic data-driven modeling framework, offering technical insights for constructing high-quality datasets, but also establishes a novel generative modeling approach for marine diesel engine fuel consumption prediction, providing robust support for intelligent engine maintenance and energy efficiency optimization.https://www.mdpi.com/2077-1312/13/6/1140fuel consumption baseline modeldiesel engine condition assessmentprobabilistic generative modeladvanced optimization strategies |
| spellingShingle | Defu Zhang Yuxuan Song Jianfeng Gao Zhenyu Shen Liangkuan Li Anren Yao Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network Journal of Marine Science and Engineering fuel consumption baseline model diesel engine condition assessment probabilistic generative model advanced optimization strategies |
| title | Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network |
| title_full | Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network |
| title_fullStr | Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network |
| title_full_unstemmed | Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network |
| title_short | Research on Ship Engine Fuel Consumption Prediction Algorithm Based on Adaptive Optimization Generative Network |
| title_sort | research on ship engine fuel consumption prediction algorithm based on adaptive optimization generative network |
| topic | fuel consumption baseline model diesel engine condition assessment probabilistic generative model advanced optimization strategies |
| url | https://www.mdpi.com/2077-1312/13/6/1140 |
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