With the rapid development of smart ships, the ship maintenance model is also changing. In order to extract the fault characteristics of diesel engine thermal parameters more easily, reduce the model’s complexity and improve the model’s accuracy, a new approach is proposed: first, the traditional convolutional neural networks (improved convolutional neural networks (ICNN)) are improved by using Meta-ACON as the activation function, improved AdamP as the optimizer, and label smoothing regularization (LSR) as the loss function, which enhances the stability of the model. Secondly, efficient channel attention (ECA) is added to achieve the mastery of global feature information, reduce the complexity of the traditional self-attention module, and enhance the model’s feature extraction ability. Lastly, the accuracy and reliability of the model are verified through ablation and comparison experiments. The accuracy rate reaches 97.6%, which is significantly improved by 32.1% compared with the original model, and the robustness of the model is verified through the introduction of noise. The experimental results demonstrate the applicability of the model in the field of diesel engine fault diagnosis.

At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis.

The effects of six control parameters, intake valve opening timing (IVO), exhaust valve opening timing (EVO), compression ratio (CR), engine speed, intake temperature, and intake pressure on engine output power, indicated specific fuel consumption (ISFC), and nitrogen oxides (NOx) emissions, are analyzed through engine simulation. The six parameters were categorized into two groups based on the degree of influence: high influence (EVO, speed and intake pressure) and low influence (CR, IVO and intake temperature). The relationship between these two groups of parameters and power, ISFC and NOx emissions was explored. Optimization was carried out for each of the two groups of parameters, and the optimization of the high impact parameters resulted in a higher diversity and wider distribution of the solution set. On the other hand, the optimization of the low-impact parameters resulted in a more concentrated distribution of the solution set, while better reflecting the trade-off between the optimization objectives. For the optimal solutions for both sets of parameters, the high-impact parameters provided significant optimization performance compared to the standard operating conditions. Although power and ISFC were optimized, the optimal solution for the low-impact parameter performed poorly with a significant increase in NOx emissions. Therefore, the parameters should be evaluated for optimization using high impact parameters to improve engine performance.