# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
## 1 Introduction
employed data-driven modeling techniques to select model regressors, construct a NARX input-output prediction model, and design robust fault detection filters and thresholds, providing an effective approach for the design and tuning of future airspeed sensor fault detection systems [15]. [PERSON] et al. introduced an LSTM-GRU model for gear fault diagnosis, using intrinsic mode functions (IMFs) for signal enhancement to extract features, achieving high diagnostic precision [16]. [PERSON] et al. proposed replacing the self-attention mechanism in Transformers with convolutional layers for rotating machinery fault diagnosis, which reduced the computational load and training difficulty. Experimental testing was conducted to confirm the model's correctness and robustness [17]. [PERSON] et al. proposed a method for rotating machinery fault diagnosis that combines Transformer and attention, demonstrating strong robustness under varying operating conditions [18]. [PERSON] et al. proposed a novel planetary bearing fault diagnosis strategy based on Synchronous Squeeze Transformation (SST) and deep convolutional neural networks (DCNNs). This approach can automatically identify planetary alignment fault types, achieving a classification accuracy of 98.3% [19]. Although the aforementioned studies have achieved significant results in fault diagnosis, research by [PERSON] et al. indicates that convolutional structures often outperform RNNsand their variants when analyzing time-series data [20; 21]. [PERSON] et al. proposed a multi-task attention temporal convolutional network (MTA-TCN) for gearbox fault diagnosis feature extraction, which improves accuracy by 20% compared to the traditional TCN method [22]. [PERSON] et al. introduced a separable-residual-block-based temporal convolutional network (TCN-RSCB) for bearing degradation information extraction, demonstrating superior bearing life prediction accuracy over other deep learning algorithms [23]. Meanwhile, it is crucial to address how the extracted fault features are integrated. [PERSON] et al. proposed incorporating both global and local features into the TCN model through an improved attention mechanism, achieving excellent fault diagnosis results for power transmission lines [24]. [PERSON] et al. proposed an intelligent fault diagnosis method for rolling bearings based on a Gramian Angular Field (GADF) and an improved dual attention residual network (IDARN). The network converts the raw signal into 2D-GADF feature images and integrates feature information using a dual attention mechanism, achieving excellent diagnostic accuracy [25]. [PERSON] et al. proposed the CTnet model for the fault diagnosis of wind turbine gearboxes and bearings. This model combines a fully convolutional autoencoder network with a Convolutional Attention Module (CBAM). Compared with advanced time-frequency analysis methods, the results show that this approach achieves high accuracy in non-stationary signal analysis [26]. [PERSON] et al. focused on CNNs and introduced a multi-head attention mechanism to accurately identify different faults in a diesel engine at various speeds [27]. [PERSON] et al. proposed avionics module fault diagnostics using a hybrid attention-based adaptive multi-scale temporal convolutional network (HAAMTCN), achieving an accuracy of 99.64%. This approach demonstrated better performance compared to existing methods [28]. Although current neural networks can achieve high diagnostic accuracy, there are still some issues. To address these problems, this paper proposes an intelligent fault diagnosis method for large marine engines based on a convolutional neural network, a temporal convolutional network, and the attention mechanism, referred to as CNN-TCN-ATTENTION. The related work and main contributions are summarized in Table 1.  
4. Fault data are difficult to obtain, and there is often an issue of data imbalance. 1. In the field of marine engine fault diagnosis, this study proposes, for the first time, the combination of a CNN and TCN.