## 4 Experimental Results
### Identification of the Amount of Ponding Volume in a Single Pipeline  
Before the CNN is trained, a finer selection of other parameters such as learning rate and batch size can be carried out. The learning rate determines the step size of adjusting weights and error reduction in the training process. Figure 6 shows the obtained results for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pipeline Number** & **Outer Diameter/mm** & **Inner Diameter/mm** & **Length/mm** \\ \hline
1\({}^{\text{\#}}\) & \(\Phi\)32 & \(\Phi\)25 & 60 \\
2\({}^{\text{\#}}\) & \(\Phi\)32 & \(\Phi\)25 & 100 \\
3\({}^{\text{\#}}\) & \(\Phi\)42 & \(\Phi\)35 & 60 \\
4\({}^{\text{\#}}\) & \(\Phi\)42 & \(\Phi\)35 & 100 \\
5\({}^{\text{\#}}\) & \(\Phi\)48 & \(\Phi\)41 & 60 \\
6\({}^{\text{\#}}\) & \(\Phi\)48 & \(\Phi\)41 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dimensions of the pipeline specimens. Figure 4: One of the sound signals recorded by the microphone. different learning rates by considering only one epoch. One epoch is a complete pass over the entire dataset. The results demonstrate that extreme values have a negative impact on accuracy. Therefore, in this work, a learning rate value of 0.01 was used, as it presented a higher accuracy and accelerated the error convergence. Table 4 shows the results of accuracy and computation time obtained using different values of batch size. The batch size determines the size of the subset of the entire dataset used in each training iteration. As indicated in Table 4, a small batch size value generates high accuracy, but results in a high computation time. On the contrary, a high value of batch size reduces the computational time, but the accuracy is negatively affected. In this regard, we chose a batch size of 30 because it provided high accuracy and a suitable computational time. Additionally, SGDM was used as the optimizer and ReLU was used as the activation function. After we selected the above-mentioned parameters, the CNN could be completely trained and validated. However, before using the dataset to train the model, the whole dataset needed to be divided into a training set and a validation set. With the dataset well partitioned, the speed of model applications can be improved. If the partitioning is not good, it can greatly affect the deployment of the model applications. Table 5 shows the results of accuracy and computation time obtained using different ratios of dataset split. This table demonstrates that the CNN model has the highest accuracy and its application speed is optimal when the dataset splitting ratio is 7:3. Therefore, in the training process of the convolutional model, 70 sets of data obtained under each experimental case were randomly selected and converted into the Mel spectrogram, then input into the CNN as the  
Figure 5: Mel spectrogram of 1\({}^{\text{\#}}\)pipeline for 6 cases: (**a**) 0 case; (**b**) 1 case; (**c**) 2 case; (**d**) 3 case; (**e**) 4 case; (**f**) 5 case. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline \hline \multicolumn{1}{c}{Fs/Hz} & 100,000 \\ Window & Hamming \\ Window Length & 2048 \\ Overlap Length & 1024 \\ FFT Length & 4096 \\ NumBands & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mel spectrogram Parameters. training set. The remaining 30 sets were input into the trained CNN model as validation sets to complete the recognition of the pipeline ponding volume. The training process of the CNN model for six ponding volume cases in the 1*pipeline is shown in Figure 7, and the recognition results are shown in Table 6.  
Figure 7a shows that, with the increase in training times, the accuracy rate increases alternately and its fluctuation is large; after the number of training times reaches 146, the accuracy rate reaches 98.34%. Figure 7b indicates that the value of the loss function decreases continuously with the increase in training times, and finally stabilizes at about 0.086.