## 3 Results and Discussion  
### Data Collection and Analysis  
The subject of research of this study is audio captured from the ground above field pipelines in various districts of the Taiwan Water Corporation. This research collected a total of 19,716 data signals. After analyzing and evaluating the results, 94 actual excavations and 5297 valid signals were recorded. At least two training acoustic signals stored in the database were input into the training model for training. The acoustic signals must be normalized, and the input includes the normalized training acoustic signals and verification acoustic signals, as shown in Figure 8. Then, according to the training results, a CNN-based diagnostic model is constructed. The acoustic signal to be measured is input into the diagnostic model to predict the accuracy and determine whether the pipeline is leaking, and the detection accuracy of the algorithm is checked to determine whether it has achieved working conditions.  
This study used a CNN to learn steady-state data from training data and used test data to test whether the autoencoder can successfully detect transient occurrences. The criterion for transient determination is that if the error between the restored signal of the autoencoder and the original signal exceeds the thresholds set in this study (the threshold is set to 16 for the length of the monitoring window and set to 13 for the sensor count), it will be judged as transient, as shown in Figure 8. In the figure, we can see that the autoencoder determines the state of the pipeline system: \"0\" is steady, and \"1\" is transient. Figure 9 is the detection result of the convolutional autoencoder in working conditions, which is the normalized value of the original signal of each sensor and the normalized value of the sensor reading at each time point. It can be seen in the figure that the test data have obvious signal changes at four time points, and the autoencoder can correctly determine that the pipeline system is in a transient state at those time points.  
Figure 8: Diagram of CNN autoencoder restoring error value and threshold.  
Figure 9: CNN autoencoder in working conditions: detection result graph.  
Next, in order to confirm that the features that can best represent the data are extracted, it is necessary to observe the restoration effect of the autoencoder as an indicator. The better the restoration, the higher the chance that the extracted features are representative. Figure 10 is the restoration result of the CNN autoencoder; Figure 11 shows the errors obtained by subtracting the restoration result from the original signal. Taking Figure 10 as an example, for the convenience of presenting the original signal and the restoration result, the figure shows the summation of the original signal after normalization and the summation of the signal readings after restoration by the convolutional autoencoder. In Figure 10, we can see that the signal restored by the convolutional autoencoder is close to the original signal. In addition, this study further analyzed the errors of the CNN autoencoder. We can see that the CNN autoencoder has a mean absolute error of about 0.1021, which confirms that the