中国船舶及海洋工程设计研究院

ObjectiveIn marine engineering, the reliable operation of marine engines is critical to ensuring maritime safety and operational efficiency. However, the complex and harsh marine environment exposes marine engines to a range of issues, including corrosion of key components, clogged pipelines, and inadequate lubrication. These problems not only degrade engine performance but also increase maintenance costs and pose a threat to the safety of ships at sea. Therefore, developing an effective fault diagnosis method, especially one capable of diagnosing faults under cross-operating conditions, is crucial. The primary objective of this study is to enhance the diagnostic performance of marine engine fault diagnosis models under cross-operating conditions. By addressing this issue, we aim to deliver a more accurate and reliable fault diagnosis solution for marine engines, thereby enhancing the overall safety and reliability of ships. MethodTo achieve this goal, a novel intelligent fault diagnosis framework based on model fine-tuning technology is proposed. This framework employs ResNet-18, a deep learning model, as the feature extractor for identifying engine faults. First, a pre-trained model is constructed using data from the source domain. The pre-training process minimizes the discrepancy between the model's predictions and ground truth labels using a suitable loss function. The Adam optimizer is employed to perform gradient descent and parameter optimization. A 1D-CNN serves as the core computational module, comprising convolutional layers, batch normalization, activation functions, and pooling layers. Subsequently, a fine-tuning strategy is applied. Specifically, the feature extraction layers are frozen while the classifier layers are fine-tuned using data from the target domain. This approach helps align the feature distributions of the source and target domains, thereby enhancing the model's performance under cross-operating conditions. The effectiveness of the proposed framework is verified using fault data from the lubrication and intake/exhaust systems of marine engines. ResultsIn fault diagnosis tasks under cross-operating conditions, the proposed framework achieves diagnostic accuracies exceeding 94% for the engine lubrication system and 98% for the intake/exhaust system respectively. Compared with the VGG-11 model used as a feature extractor, the ResNet-18-based framework outperforms it significantly in terms of diagnostic accuracy. The ResNet-18 model shows superior stability and robustness in different transfer tasks, exhibiting lower variance in classification accuracy. ConclusionIn conclusion, the proposed intelligent fault diagnosis framework based on model fine-tuning technology significantly enhances diagnostic performance under cross-operating conditions for marine engines, improving the model's generalization and adaptability to diverse operating scenarios. However, this study has certain limitations. Future work may focus on integrating agent technology to improve the model's interactive and text understanding capabilities, enhancing the model's ability to identify and diagnose unknown fault types, and conducting experiments on cross-device transfer diagnosis for diesel engines. This will further improve the practical application value of the fault diagnosis technology for marine engines.

The numerical simulation and model test study is conducted on yawing motion characteristic for the 40000 DWT bulk carrier under the combined conditions of wind,wave and current cases.This research analyzed the response of yawing motion and maximum cable tension of anchor chains.Sea condition and length of anchor chains could have influence on them.The test results can provide important technical reference for the safety mooring of single anchored ships.

ObjectivesDuring side-by-side operations of two floating bodies, significant wave oscillations within the gap and large-amplitude motion responses of the floating bodies can easily occur because of their proximity under hydrodynamic interactions, which pose serious threats to operational safety. Therefore, this study investigates the hydrodynamic interference characteristics between two floating bodies of different dimensions. MethodsA hybrid two-way coupled field-domain decomposition method was employed to establish a numerical wave tank, combined with the overset technology to simulate the motion characteristics of the two floating bodies. The study focused on the hydrodynamic interference between two floating bodies of different dimensions in regular waves. First, the hydrodynamic resonance phenomenon in the gap between two floating bodies of similar dimensions was numerically simulated and compared with experimental data to validate the accuracy of the numerical model. Subsequently, the heave motions of two floating bodies with different dimensions in regular waves were simulated, and the effects of different floating body arrangements on the vertical motion responses of the floating bodies were analyzed. ResultsThe results indicate that, the hybrid two-way coupled field-domain decomposition method, validated against experimental data, offers higher computational efficiency compared to the CFD method while maintaining accuracy. At high wave frequencies, the two floating bodies tend to exhibit anti-phase motion. Additionally, when the larger floating body is positioned upstream, it provides a significant shadowing for the smaller floating body downstream. ConclusionsThis study employs the hybrid two-way coupled field-domain decomposition method to validate the hydrodynamic interference model between two floating bodies. It reveals the occurrence of anti-phase motion of the two floating bodies at high wave frequencies and the shadowing effect when the larger floating body is upstream, providing theoretical guidance for optimizing floating body arrangements and enhancing operational safety.

In response to the instability issues of traditional neural networks in handling time-dependent dynam-ic processes and noisy data,a dynamic force reconstruction method based on long short-term memory(LSTM)networks was proposed.The measured response signals,contaminated by noise,were normalized as input vari-ables,while the normalized dynamic loads as output variables.The implementation approach of LSTM networks was adopted.To enhance the network's generalization ability,various types of dynamic responses and original loads were defined as sample structures at each time step.In view of interval uncertainty,the point distribution strategy results were adjusted to build the dimension-wise method(DWM)based on the traditional point distri-bution methods,to get precise resolution of uncertainty load identification with independent interval variables in the investigation of uncertainty variables in a specific dimension through fixation of others.Finally,by nu-merical examples and a comparison with traditional neural networks(back-propagation neural networks),the LSTM neural network was proved to be more stable in handling noisy data.An experimental design validates the effectiveness and feasibility of this method for time-dependent data.