# Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
## 4 Pipeline Fault Experiment Results Analysis  
Figure 4 shows an example of the AE signal collected from two cases of normal and 0.3 mm leak. The signal from the 0.3 mm drill hole shows more appearance of bursts than the normal case does because of the turbulent flow from the leakage position. Considering four different leak levels--including a 2, 1, 0.5, and 0.3 mm drill hole--for the leak-off testbed system set up, each type of fault signal is applied to the burst detector to determine the burst segment signals. Then, the features are extracted from the burst segment and input to the classifier. Figure 5 depicts the result of the proposed burst detection algorithm with the leakage signal segment. It is observed that the ECFAR can detect most of the bursts or impulses which occur in the signal. Since the threshold from the ECFAR detector is more adaptive to the noise, it can efficiently detect the true impulses. The classification result is shown in Figure 6a with the confusion matrix for evaluating the accuracy of the OVA-MCSVMs classification of the proposed method. As shown in Figure 6a, the mean accuracy of the classifying algorithm is 93%. For comparison, the research also employs the wavelet threshold for burst detection. The wavelet threshold is another algorithm utilized for impulse and burst detection, as described in [21]. After the impulse detection stage, the same process of feature extraction and classification is applied to the burst waveform. The results of the leakage signal classification, using the wavelet threshold as the method of burst detection, are shown in Figure 6b. The results show that the proposed method using ECFAR renders better results in comparison to the wavelet threshold. Moreover, in both methods of burst detection, most of the misclassification occurs between the 2 mm crack and normal classes, which is due to the high-flow rate of water leakage substantially reducing the pressure inside the pipe. This pressure decrease induces the decrease in AE signal amplitude, which is generated by the stress of turbulence flow at the leakage point. As a result,  
Figure 4: Examples of collected acoustic emission signals for normal case and 0.3 mm leak. the leakage signal is similar to the background signal. This phenomenon can be observed in the wavelet scalograms, which are extracted from the signals of different levels of leak, as shown in Figure 7.  
As in Figure 7, the number of salient peaks against the background signal from the 2 mm class is significantly decreased compared to those of the 1 mm class. However, compared with the normal signal, the density of impulses in the 2 mm class was higher. To verify the effectiveness of the proposed method, the obtained results are compared with those of two other state-of-the-art methods, including k-nearest-neighbor (k-NN) [6] and multi-layer perceptron neural network (MLP) [11]. The results are shown in Table 2, where the comparison is made in three scenarios: (1) only extracting the statistical features from raw signal without applying burst extraction, (2) applying the burst extraction using wavelet threshold, and (3) applying burst  
Figure 5: Performance of ECFAR burst detection algorithm when applied to the leak signal. Figure 6: Confusion matrices of classification result for (**a**) the proposed method for impulse detection with ECFAR and (**b**) wavelet threshold-based impulse detection. extraction using ECFAR. The classification accuracy is defined as mean classification accuracy (MCA), which is calculated by  
\[MCA=\frac{\sum_{N_{\textit{classes}}}N_{\textit{truePositive}}}{N_{\textit{testSample }}}\times 100(\%) \tag{6}\]  
where \(N_{\textit{testSample}}\) is the total number of samples used to calculate the classification accuracy of the proposed method in a particular dataset, \(N_{\textit{classes}}\) is the total number of fault types (leak levels), and \(N_{\textit{truePositive}}\) is the number of datapoints in class \(i\), which is correctly classified as class \(i\).