# Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
## 1 Introduction  
As the main power source for ship navigation and power generation, ship engines occupy an important position in the field of ships. Due to its importance, if the engine operation failure cannot be found in time, it will cause huge economic losses or even casualties. Therefore, timely monitoring and diagnosis of the engine's working condition can effectively improve the reliability, economy, and safety of normal ship operation. With the wave of the era of ship intelligence and the continuous development and improvement of artificial intelligence and algorithmic arithmetic power, more and more scholars apply artificial intelligence to the field of condition detection and fault diagnosis, and the fault diagnosis methods of ship engines also progress and develop. Fault diagnosis of ship engines faces challenges such as data scarcity, multiple fault types, data noise interference, and insufficient labeling information. In order to provide safety for normal ship navigation, [PERSON] et al. [1] introduced deep learning into ship diesel engine fault diagnosis and proposed a fault diagnosis method based on correlation distribution and deep confidence networks, which achieved good results. However, this type of method does not take into account the problem of imbalance in the ship fault data categories, resulting in some defects in the training and application of the model. Aiming at the above problems [PERSON] et al. [2] first used principal component analysis (PCA) to reduce thedimensionality of the data. Then, the sample size optimization algorithm (SSO) was used to solve the problem of data imbalance, and the three-dimensional Arnold mapping to the particle swarm optimization algorithm was introduced to improve the holding vector machine (SVM), which effectively improves the generalization ability of the classification model. Finally, the model is validated by example using the fault data of the fuel supply system, and the experimental results show that the method is able to alleviate the impact of unbalanced data. [PERSON] et al. [3] proposed a semi-supervised principal component analysis (SSPCA) to improve the diagnostic accuracy of the model by fusing labeled and unlabeled samples as an alternative to unsupervised learning, with better robustness to false alarms. The above model has achieved good results in fault monitoring, but it can only determine whether the fault occurs or not and cannot diagnose the specific cause of the fault, which makes it difficult to play a guiding role for the subsequent operation and maintenance decision-making work. In this regard, [PERSON] et al. [4] established a fault tree, transformed the fault tree into a Bayesian network, constructed a Bayesian network diagnostic model of the lubrication system, and, in the example validation, the model was able to accurately diagnose the cause of the failure. [PERSON] et al. [5] introduced a convolutional neural network to solve the problem of propulsion fault detection and classification of time series. [PERSON] et al. [6] fused an artificial neural network (ANN) model, a belief rule-based reasoning (BRB) model, and an ER rule model and used a genetic algorithm to optimize the importance weights of each model to improve the overall performance of the fusion system, and finally, through the three models, joint decision-making to realize the fault diagnosis of the ship system. In recent years, algorithmic improvements have achieved significant results in the field of industrial fault diagnosis [7; 8; 9; 10; 11]. [PERSON] [12] proposed a new kNN-based classifier (PFS-kNN) to find the k-nearest neighbors using [PERSON]'s metric of the image fuzzy soft matrix, which was validated on the UCI medical dataset, and the PFS-kNN outperformed the most state-of-the-art kNN-based algorithms. [PERSON] et al. [13] proposed a diagnostic method based on fault sign simulation to solve the problem of a too-small sample size of faults in the fault diagnosis of a particular engine. [PERSON] et al.