# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
## 1 Introduction
The CNN is used to extract local spatial features, reduce redundant information, and handle noise, while the TCN captures complex temporal features, enhancing the model's ability to recognize fault patterns. This approach effectively improves the fault feature extraction capability. 2. By introducing the attention mechanism to optimize feature selection based on the CNN-TCN, this study addresses the issue of fusion between different fault features, adapts to various fault patterns, demonstrates the model's generalization ability, and improves computational efficiency. 3. Through ablation experiments, noise experiments, and comparative experiments, the reliability and robustness of the proposed method have been validated. 4. By using simulation modeling, accurate simulations of several typical faults in large marine engines were conducted, thereby obtaining a fault dataset for large marine engines. This approach addresses the challenges of insufficient fault data and imbalanced data distribution in large marine engines. \begin{table}
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**Existing Issues** \\ \hline
1. The systems of large mechanical equipment are complex, with numerous fault types and fault feature types, which leads to the automatic feature extraction by deep learning models often containing redundant information. This increases the computational complexity of the model and affects its performance. Additionally, some systems exhibit very subtle behaviors in time series, and existing deep learning methods face difficulties in capturing such complex fault patterns. 2. In many deep learning models, feature extraction often focuses on a single fault pattern. However, in real-world fault diagnosis tasks, faults are typically diverse and combinatorial. Existing methods perform poorly in feature fusion when dealing with data from different sensors (such as pressure, temperature, flow rate, etc.). 3. In practical fault diagnosis, the data are often accompanied by noise and disturbances, and deep learning models are sensitive to these noises during the feature extraction process, resulting in poor model robustness. 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. The CNN is used to extract local spatial features, reduce redundant information, and handle noise, while the TCN captures complex temporal features, enhancing the model’s ability to recognize fault patterns. This approach effectively improves the fault feature extraction capability. 2. By introducing the attention mechanism to optimize feature selection based on the CNN-TCN, this study addresses the issue of fusion between different fault features, adapts to various fault patterns, demonstrates the model’s generalization ability, and improves computational efficiency. 3. Through ablation experiments, noise experiments, and comparative experiments, the reliability and robustness of the proposed method have been validated. 4. By using simulation modeling, accurate simulations of several typical faults in large marine engines were conducted, thereby obtaining a fault dataset for large marine engines. This approach addresses the challenges of insufficient fault data and imbalanced data distribution in large marine engines.