# Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
## 4 Conclusions  
In this paper, a new MPGANN model is proposed, and in the experiments on the ship engine dataset, firstly, different graph structure construction methods are compared, and the fused graph structure enhances the expressive ability, especially the fusion of probabilistic and ordinal graph structures, which dramatically improves the diagnostic accuracy of the model. Secondly, the accuracy of the model under different activation functions, head counts, and learning rates is greatly different, which shows that the influence of hyperparameters on the model is also not negligible. Finally, when MPGANN is compared with other algorithms, it outperforms the various methods compared in diagnosis in terms of accuracy, precision, recall, and F1 score. Therefore, the MPGANN proposed in this paper has a certain practical significance in the field of intelligent ship fault diagnosis. In summary, the research for this paper is summarized as follows:  
1. The two graph structure construction methods in the MPGANN model can effectively obtain probabilistic similarity and ordinal similarity between data samples and transform the data into probabilistic graph structure and ordinal graph structure.
2. Early fusion is employed to combine the probability map structure and rank-order map structure with the incorporation of feature weights. This integration process effectively amalgamates information from samples at various scales.
3. The multi-head attention mechanism is applied to conduct multi-channel feature screening on the fusion graph structure, extracting feature information with higher relevance to enhance the diagnostic performance of the model.
4. The model effect was validated using the ship engine fault dataset, and compared with other models in terms of accuracy, precision, recall, and F1 score, MPGANN was the most effective, with a diagnostic accuracy as high as 97.58% and an F1 score of 97.6%.  
The proposed diagnostic model in this paper fuses features from different aspects of the data, mining the information of the dataset at a deeper level, but it also has some limitations, such as: the fused features contain only probabilistic similarity and rank-order similarity; the fusion weights need to be set in advance; and so on.  
There are three goals for future work: (1) Physical connections in real space will also imply information, and the actual structural connections of the engine will be transformed into graph structures to mine the correlation information between the samples. (2) Choose more graph fusion methods or assign meanings to edge features so that the model has more information for training to improve its diagnostic accuracy. (3) Introduce the attention mechanism into the fusion process to achieve adaptive allocation of fusion weights.  
Conceptualization, Z.A. and H.C.; methodology, Z.A.; validation, Z.A., H.C. and J.W.; formal analysis, H.C.; investigation, Z.C. and J.W.; resources, H.C.; data curation, Z.A., L.W. and K.J.; writing--original draft preparation, Z.A.; writing--review and editing, Z.A., H.C. and L.W.; visualization, Z.A. and Z.C.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project: Development of Ship Operation Condition Monitoring and Simulation Platform (funding number: 1638882993269) and the research and application of the Smart Ship Digital Twin Information Platform (funding number: 2022)H1/10800097).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: Author [PERSON] was employed by Dalian Maritime University Smart Ship Limited Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.