## 4 Experimental Validation
FID represents the diversity and quality of generated objects. The smaller the FID, the better the diversity and quality. In the later tests, in order to highlight the overall trend and distribution characteristics, we use bold to mark the best results in different test schemes. Next, different data grouping forms are tested to see which grouping situation has the best effect on data feature learning and recognition, and the data quality is the highest. We will adjust the number of groups and proportion relationship of three types of data, namely include intact data, hole defect data and crack defect data. The best proportion of data sets with the best learning ability for these three situations will be found by testing in different situations. The design and allocation of the experiment are shown in Table 3, the best values in each test result are marked in bold:  
Through the table, we can observe that the fourth group should be selected for experiment and training. In this grouping case, the lowest FID value and the highest accuracy rate can be obtained, and the higher recall value can also be maintained, indicating that the learning and training effect of input data is the best in this case. After determining the classification method of data groups, use the classification method with the best learning effect mentioned above, and use the data collected from the experiment to make a training set. First, the generation model is used to expand the unbalanced data of some samples, and then the expanded data is mixed with the original data to make a new dataset. Then put, the new data set obtained after learning and training into the previously selected classification network for testing, and verify the overall experimental design through the accuracy and effect of classification. Relevant details of the training process are supplemented as follows. For the dataset, we randomly divided the dataset into five parts and used one of them as test data for each run. We also randomly split the remaining data into train and validation data with a ratio of 6:1. For the training of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Intact Data** & **Hole Data** & **Crack Data** & **FID** & **Recall** & **Accuracy** \\
**Volume** & **Volume** & **Volume** & & & \\ \hline
20 & 80 & 80 & 26.23 & 0.51 & 78.16 \\
40 & 80 & 80 & 38.04 & 0.62 & 84.00 \\
80 & 80 & 80 & 18.15 & **0.78** & 87.15 \\
160 & 80 & 80 & **12.37** & 0.72 & **88.37** \\
320 & 80 & 80 & 16.35 & 0.31 & 87.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different data grouping forms and test results. all tasks, we normalize each feature to have zero mean and unit variance. We set the batch size as 16 and the number of epochs as 200. We used Adam optimizer with a learning rate of 0.001. The core values in the diffusion model are also given here. We set the number of the diffusion step T = 100, the minimum noise level \(\beta_{1}\) = 0.0001, and the maximum noise level \(\beta_{\text{T}}\) = 0.5. For the several types of data enhancement and data classification methods used in this study, grouping and collocation tests are conducted, respectively. This is to test which data enhancement and data classification methods work best. After determining the testing plan and training details, extensive training and testing were conducted for the five methods proposed earlier in this experiment. The framework combining data augmentation and classification algorithms (including the improved DDPM) proposed in this paper, as well as four different algorithm combinations: \"VAE + DenseNet\", \"DCGAN + DenseNet\", \"DDPM + ResNet\", and \"DDPM + DenseNet\" (including the original DDPM), were tested as a whole. In addition to the accuracy index, the FID index was also retained as a reference in the final test results. After multiple rounds of training, the results are shown in Table 4 and the best values in each test result are marked in bold:  
It can be seen that the improved DDPM model is the best for learning and generating such defect signals.