## 5 Conclusions  
In this study, a method for identifying and classifying composite damage signals based on DenseNet classification and DDPM data enhancement is proposed. At the same time, a dimension transformation method is introduced to process defect signals. This method converts one-dimensional timing signals into two-dimensional images, which is convenient for combining the data with the latest and most mature neural network system. This method, by adding attention mechanism and other related parameter designs, has been successfully improved. After determining the test plan and training details, the five application methods proposed at the end of this experiment were fully trained and tested, and the advantages and disadvantages of the proposed method and the other combination methods were compared. The experiment and test results show that the method designed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models Used** & **FID** & **Accuracy** \\ \hline VAE + DenseNet & 41.65 & 71.25 \\ DCGAN + DenseNet & 33.56 & 86.17 \\ DDPM + ResNet & 28.33 & 85.42 \\ DDPM + DenseNet & 25.10 & 86.37 \\ Proposed framework & **13.74** & **88.10** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of different combinations of data enhancement and data classification methods.  
in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify the damage signals of common defects in CFRP composite plates, and can achieve high accuracy. In the final comprehensive test, the test results of the method proposed in this study for Lamb wave signal-datasets of different defects reached 88.10%. Compared with other common data expansion and signal classification methods, this scheme also has advantages, and is practical for the nondestructive testing of composite materials. The method proposed in this experiment mainly focuses on the study of micro-damage in composite structures, and it may not be able to detect defects smaller than 1 mm. The application object of this method is mainly carbon fiber composites, but it can also be applied to other composite materials, such as glass fiber, and has been tested, whereas other materials have not been tested yet. In the future, we may try to extend it to other materials and other types of damage, and further improve the accuracy. We believe that this scheme will continue to improve accuracy in the future and will be extended to more types and components of composite products; it may play a role in composite nondestructive testing and structural health monitoring.  
Validation, Z.L.; Investigation, Y.H.; Writing-original draft, T.H.; Writing-review & editing, Y.H.; Supervision, Y.G. and F.X.; Funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the National Natural Science Foundation of China (Grant Nos. 52275146, 51835003, 61804054 and 12174102), supported by State Key Laboratory of New Textile Materials and Advanced Processing Technologies No. XXXFZ2022006, and supported by \"the Fundamental Research Funds for the Central Universities\".  
Not applicable.  
The authors declare no conflict of interest.