## 2 Background Study on Pipeline Leak Detection Methods
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)
introduced a leak-detection method using a MFCC and a 2 DCNN for acoustic signals collected using listening devices [10]. The schematic representation of the same is in Figure 2.

The flow of the layers in the schematic shown in Figure 2 is input, convolutional, max pool, convolutional, max pool, fully connected, and output. The input for the 2 DCNN architecture is the acoustic signal's MFCC. The convolutional layer's filter size is 32, with a kernel size of 2 \(\times\) 2 and an activation function ReLU. The pool size of both max pool layers is 2 \(\times\) 2. The number of neurons in the fully connected and output layers is 128 and 2, respectively. ## 3 Experimental Setup

For this paper, the experimental arrangement, located in UTAR, Malaysia, helped to collect data. The experimental configuration comprises a 1-kiloliter water reservoir, a water pumping system with a capacity of 4 kW, and a 40 m pipeline (galvanized iron) with specifications of inner, outer, and simulated leak aperture diameters of 80, 90, and 5 mm, respectively. The simulated leak diameter is 5 mm, and there is one acousto-optic sensor [52]. Five components of the acousto-optic sensor are a 980/1500 nm wavelength division multiplexer (WDM), a 980 nm pump, fiber laser cavity/sensing fiber, Erbium-doped fiber, and a photodetector. The 980 nm pump helps deliver the laser particles into sensing fiber via the 980/1550 nm WDM. The laser-filled sensing fiber on the pipeline measures acoustic vibrations, and the photodetector detects the vibration. The diagrammatic representation of the experimental arrangement is in Figure 3. Figure 4 shows a sensing fiber positioned on the aperture for leak simulation, and Figure 5 shows the experimental arrangement. System specifications used to collect the data, data preprocessing, training, testing, and predicting, are below:

* Processor: AMD Ryzen 5 4600 HS with Radeon Graphics, Nvidia GeForce GTX 1650 Ti;
* Memory: 24 GB;

Figure 3: Experimental arrangement’s diagrammatic representation. \[S(t)=\sum_{k=1}^{K}\widetilde{I}_{k}+r_{k} \tag{3}\]0 MFCC and 2 DCNN for leak detection. * System type: 64-bit operating system, x64-based processor;
* Operating system: Windows 11. L in Figure 3 is the length between the leak aperture and the sensor. In the event of a pipeline leak, vibrations occur. The sensor records this vibration. This paper uses two sets of data, which are each split into four classes. The water pressure of the first set is 2-bar, and the second is 3-bar. At these two different pressures, the four classes of collected data are no leak, leak at a 1 m distance, leak at a 2 m distance, and leak at a 3 m distance. The non-leak data are captured with the sensor placed 1 m from the leak point on the pipeline setup with the leak aperture closed. Each set comprises 1200 data samples collected over a 50-millisecond interval. Thus, the recorded samples are 4800 in a single pressure set. Therefore, 9600 samples are in both pressure sets. The response changes based on the distances at which the sensors are connected. ## 4 Proposed Leak Detection and Localization Method

Four steps are involved in the proposed novel EMD with an optimal mode selector, an MFCC, and a 2 DCNN. The proposed leak detection and localization method's steps are as follows: (1) Decomposing the data collected from the sensor using EMD; (2) Using the novel optimal mode selector algorithm to choose the desired IMF based on the energy of the IMF; (3) Converting the one-dimensional data to two-dimensional data and extracting essential features using an MFCC by capturing the frequency components; (4) Passing the MFCCs to the 2 DCNN for leak detection and localization. The proposed architecture's workflow is shown in \[S(t)=\sum_{k=1}^{K}\widetilde{I}_{k}+r_{k} \tag{3}\]1

\[S(t)=\sum_{k=1}^{K}\widetilde{I}_{k}+r_{k} \tag{3}\]2 Sensing fiber positioned on the leak aperture. \[S(t)=\sum_{k=1}^{K}\widetilde{I}_{k}+r_{k} \tag{3}\]3 Experimental arrangement.