## 4 Results and Analysis  
An AE dataset acquired from a spherical storage tank was used in this study to validate the effectiveness of the proposed model. The dataset consists of AE signals collected under two different conditions: Normal and with a 3 mm diameter pinhole crack. The purpose of the experiment is to identify whether each signal belongs to the normal condition or the cracked condition. Figure 7 shows the acoustic signals under the two conditions. To check the presence of the crack, we first analyze the AE signals. The rise angle of the obtained AE signals under the cracked condition was first observed because it was directly affected by the presence of the crack. When a crack develops on the surface of a tank, the effect of the event propagates as an elastic wave. We can observe the activity with the help of rise angle of the AE waveform. In Figure 8, the impact of the AE activity on signal rise angle can be observed. The delay between the onset of damage and the peak amplitude is short, which is termed as high-rise angle caused due to the volumetric change; hence it proves the presence of the crack. As is evident in Figures 7 and 8, classification using raw AE signals in their original form (time-domain values) is not an easy task. Therefore, we extracted the time-domain statistical features from the AE signals. These time-domain statistical features project the AE signals into a reduced features space. The features extraction step is also needed to extract discriminant information from the signals. In the next step, the extracted time-domain statistical features were provided to the support vector machine (SVM) binary classifier. The kernel function used during training and validation of the model was a radial basis (Gaussian) kernel. To obtain stable experimental results, we used a cross-validation technique. In cross-validation, a sample dataset is uniquely divided into 60% training samples and 40% test samples. Statistically and computationally, it is important to use a \(K-fold\) cross validation technique. In \(K-fold\) cross validation, the dataset is divided into \(K\) subsets. Each subset is used once for testing and \(K-1\) times for training. In Figure 9, the feature vectors belonging to the two classes are separated by the SVM decision boundary. The blue samples belong to the normal condition and the red samples belong to the crack condition. It can be observed that the instances of the two classes were well identified and properly separated. Therefore, in turn, the classification accuracy of the developed model was also enhanced. \begin{table}
\begin{tabular}{c c c} \hline \hline  & Peak sensitivity (V/ms); [V/\(\upmu\)bar]: 96[–25 dB] \\ Acoustic Emission sensor (WDI-AST sensor) & Operating frequency range: 200 to 900 kHz \\ Directionality: \(\pm\)1.5 d \\ BResonant frequency: 650 kHz \\ \hline \multicolumn{3}{c}{18-bit 40 MHz A/D conversion} \\ PCI board with 2-channel AE sensor & AE input: 2 channels (one is 10 M samples/s rate, and another is 5 M samples/s as two channels, with simultaneously used)} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental setup details. PCI: peripheral component interconnect; WDI-AST: wideband differential; AE sensor. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal Type** & **Fault Location** & **Pinhole Size** & **Percentage of** & **Percentage of** & **Number of** \\  & **(mm)** & **Training Samples** & **Test Samples** & **Channels** \\ \hline Normal & None & 0 & 60 & 40 & 4 \\ \hline Pinhole crack & Bottom of the tank & 3 & 60 & 40 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset description. To further verify the effectiveness of the proposed model, we compared the classification results of the proposed model with those of a state-of-the-art machine-learning algorithm, that is, a backpropagation neural network (BPNN) algorithm. A two-layered BPNN was developed with 10 neurons in each layer. We replaced the SVM in the proposed method with the BPNN to create a similar work flow.