# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
## 1 Introduction  
Navigation safety is crucial for ships, as it forms the foundation of all on-board operations. As the primary power source of a ship, the marine diesel engine operates in harsh environments, and its performance inevitably degrades over time. A reduction in engine performance or a malfunction can negatively affect the vessel's navigation tasks and potentially lead to significant losses [1]. Therefore, the fault diagnosis of marine diesel engines is particularly important for ensuring maritime safety. Research on deep learning-driven fault diagnosis in marine diesel engines involves processing and analyzing fault data generated during engine operation, which enables precise fault diagnosis and provides valuable insights for repair work [2]. Such research not only enhances the safety and reliability of ship navigation but also improves operational efficiency and reduces maintenance costs [3]. In the context of intelligent vessels requiring advanced operational maintenancefor engine room equipment [4,5], this study has significant practical implications and application prospects. In recent years, numerous researchers have achieved notable advancements in the area of fault diagnosis using deep learning. This is attributed to the outstanding capability of deep learning-based algorithms to autonomously learn from large datasets and detect faults [6]. [PERSON] et al. proposed a combined model of the Recursive Graph Optimization Convolutional Neural Network (RP-CNN) for the online fault diagnosis of MDE bearing wear. The proposed model uses a recursive graph (RP) to represent the nonlinear features of the input signal, combining it with a CNN, and achieves a diagnostic error rate of less than 5% under various operating conditions [7]. [PERSON] et al. proposed a method based on a CNN for multi-cylinder diesel engine cylinder misfire fault classification. This approach is suitable for engines with different cylinder configurations of the same type, showing a certain degree of generalization. The method uses one-dimensional convolution to achieve a low computational complexity and short prediction time [8]. [PERSON] et al. proposed a random deep CNN (SDCNN) approach, utilizing random oversampling to address the issues of imbalanced fault distribution and multi-fault confusion [9]. [PERSON] et al. proposed the MSCNN-LSTMNet model, which combines multi-scale long short-term memory (LSTM) networks with convolutional neural networks (CNNs) for diagnosing diesel engine misfires. The model includes a residual module for denoising raw cylinder vibration signals, which are then sent to the LSTM to extract temporal features further. It performs well in four noisy datasets, demonstrating high accuracy and improving model robustness and generalization capabilities [10]. [PERSON] et al. proposed an improved CNN model, MBCNN; this is a convolutional neural network (CNN) with several branches that incorporates cross-entropy loss. This model has been shown to improve accuracy compared to traditional classification algorithms, achieving 100% accuracy [11]. [PERSON] et al. combined a CNN and LSTM to enhance feature extraction while using the Bayesian algorithm to optimize hyperparameters, addressing the issue of hyperparameter setting in deep learning models. The approach achieved high accuracy in hydro-turbine fault diagnosis [12]. [PERSON] et al. proposed a 1 DCNN-GRU model for refrigeration unit fault diagnosis. Through experimental parameter tuning, they achieved high accuracy with very few iterations [13]. [PERSON] et al. proposed a graph convolutional network (GCN)-based approach, which was first applied to tasks such as the fault detection of sensors within the SFDIA framework. The experimental results demonstrated the method's advanced performance [14]. [PERSON] et al.