# Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
## 1 Introduction
[14] proposed a data-driven model for predictive health maintenance to monitor the health status of the equipment and take action before a fault occurs. The model will alert for any major defects in the system and can be effective in avoiding work interruptions in the production process. A graph neural network (GNN) is a neural model that captures graph dependencies through message passing between graph nodes. [PERSON] et al. [15] divided GNN applications into six directions: natural language processing, computational technology, natural science research, knowledge graph, combinatorial optimization, and graph generation, and made a more comprehensive and detailed summary of GNN applications. In recent years, variants of GNNs such as Graph Convolutional Networks (GCNs) [16] and GANN [17] have achieved breakthroughs in many deep learning tasks. [PERSON] et al. [18] applied GCNs to fast and scalable semi-supervised classification of nodes in graphs, which demonstrated significant superiority over a large number of datasets. [PERSON] et al. [19] proposed a multi-graph-based GCN fault diagnosis method that fuses two feature embeddings into one combined embedding by introducing a self-attention mechanism to improve classification accuracy and stability in unbalanced datasets. [PERSON] et al. [20] proposed a domain adversarial graph convolutional network (DAGCN), which incorporates three types of information--class label, domain label, and data structure--into a deep network for modeling, thus realizing unsupervised domain adaptive (UDA) fault diagnosis. [PERSON] et al. [21] proposed a new quantum graph convolutional neural network (QGCN) model by drawing on quantum neural networks and graph convolutional neural networks, which utilize quantum parametric circuits and the high computational power of quantum systems to accomplish the traditional task of graph classification in machine learning. In all of the above studies, GCN has achieved good results in the field of graph structure classification due to its advantage of being able to handle non-Euclidean distance data well. However, GCN'smean-aggregation operation in the metric matrix makes it insufficiently attentive to important nodes. Therefore, [PERSON] et al. [22] constructed GANN by adding a self-attention layer to traditional GCNs, which are able to set different weights for different nodes in the neighborhood, thus solving the drawback that GCNs cannot consider the importance of neighbors. On this basis, [PERSON] et al. [23] proposed a full graph attention neural network (FGANN), which also considers the influence of nodes other than neighboring nodes and can handle the graph classification task well. [PERSON] et al. [24] noticed that in sentiment classification tasks, traditional models are not sensitive to syntactic structural information due to the complexity of syntactic analysis relationships, and the models lack the utilization of external sentiment knowledge. In this regard, a new graph attention neural network is proposed that realizes attention to multiple levels of syntax, semantics, and knowledge by introducing the mechanism of multi-head attention and acquires knowledge about sentiment by considering information such as syntax. The relationship between numerous thermal parameters of ship engines is not just a simple linear relationship but is often very complex and agnostic. In this paper, to address this problem, graph learning theory is introduced to explore the relationship of geometric structure among data, and a MPGANN ship engine fault diagnosis model is proposed. Based on the ship engine failure dataset, the model uses both T_SNE and Spearman to construct the probabilistic graph structure and ordinal graph structure, selects appropriate weights for early fusion of the two graph structures, and finally adopts GANN to transfer and extract the combined features.