# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
## 4 Conclusions  
This paper addresses the current challenges in marine diesel engine fault diagnosis, such as insufficient accuracy and inadequate feature extraction, by introducing the CNN-TCN-ATTENTION model. This novel intelligent fault diagnosis approach for marine engines combines deep learning techniques. The conclusion of the approach applied in this paper is as follows:  
1. Given the difficulty of obtaining fault data for marine diesel engines, a modeling and simulation approach is employed to extract fault data that meet the requirements of fault diagnosis.
2. The combination of a CNN and TCN for feature extraction is applied for the first time in the field of marine diesel engine fault diagnosis to improve feature extraction capabilities. This approach first uses the CNN to extract global features, followed by the TCN to capture temporal sequence features. Additionally, an attention mechanism is introduced to address the issue of insufficient feature fusion. This mechanism effectively assigns different weights to important features, thereby achieving feature fusion and improving the model's accuracy.
3. To validate the effectiveness of the CNN-TCN-ATTENTION model, three different experimental cases were carefully designed. Specifically, in the ablation experiment, the quantity of convolution layers in the CNN network was adjusted through a controlled variable method to find the optimal layer distribution scheme. Additionally, by removing each component of the model, the accuracy of the CNN-TCN-ATTENTION model achieved 100%, outperforming the other ablation models. In the noise experiment, different types of noise were added to observe the model's accuracy trend, demonstrating the robustness of the proposed model. In the comparative experiment, the CNN-TCN-ATTENTION model was compared with other deep learning models, and the CNN-TCN-ATTENTION model surpassed all other models in accuracy, confirming the suggested method's efficacy.  
In conclusion, the approach suggested in this paper demonstrates significant advantages in addressing the challenge of insufficient feature extraction in marine diesel engine fault diagnosis by combining feature extraction models. This work provides valuable theoretical insights for the advancement of fault diagnosis in marine diesel engines.  
Moreover, in practical applications, annotated fault datasets are often scarce, and it is challenging and expensive to manually label a large number of defect samples. Therefore, we plan to enhance the CNN-TCN-ATTENTION model with feature-based transfer learn ing methods to apply it to other fault diagnosis tasks with limited labeled data [41; 42]. By aligning the characteristics of the target and source domains in a shared area, the model will learn domain-invariant features, ultimately improving its adaptability.  
Conceptualization, A.M.; methodology, A.M.; software, A.M.; validation, A.M., J.Z. and H.S.; formal analysis, A.M. and H.S.; investigation, A.M. and H.S.; resources, J.Z. and H.S.; data curation, A.M.; writing--original draft preparation, A.M.; writing--review and editing, A.M., Y.C., H.X. and J.L.; visualization, A.M.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China National Major Research Instrument Development Project (grant number: 6212780).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
Author [PERSON] is affiliated with Dalian Maritime University Smart Ship Limited Company. The other authors declare no commercial or financial relationships that could be seen as potential conflicts of interest.