# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 3 Experimental Setup  
_Test Setup for Pipeline_  
The experimental setup and its schematics are presented in Figures 6 and 7. A stainless-steel pipeline, with 6 mm of thickness and 144 mm outer diameter, was fitted with sensors R15I-AST from Mistras Group, Inc., West Windsor Township, NJ, USA, to simulate pipeline leaks. The data acquisition system of National Instruments, model NI-9223, was used to collect AE signals at a 1 MHz sample rate; detailed specifications of the test setup are presented in Table 3. An electric drill was used to drill holes, simulating leaks of different sizes. For controlling the fluid flow, a fluid control valve was welded at each hole. Water was selected for the experiment because it is safe and environmentally friendly due to its non-hazardous and non-toxic properties. The parameter settings directly influence the quality of captured AE signals and, consequently, the final analysis results. Deviations in frequency range, resonant frequency, or placement could  
The sensitivity and frequency range of the sensors significantly affected the data quality. Sensors with a higher sensitivity, e.g., 75 kHz resonance frequency, extracted AE signals more effectively, improving the scalograms clarity and classification performance. Additionally, sensor placement closer to potential leak sources resulted in better signal strength, contributing to higher model accuracy. Initial measurements were conducted with the valve closed for one minute to establish baseline conditions. Following this, the valve was opened to simulate a 1 mm hole in the pipeline, and measurements were recorded for an additional two minutes. Further data collection was performed with the valve closed at a pressure of 18 bars for one minute. Subsequently, a 0.5 mm leak was intentionally introduced, and data were collected for two minutes with the valve open. This process was repeated for leak sizes of 0.7 mm and 1 mm at a reduced pressure of 7 bars. For each fluid pressure level, a total of 360 samples were collected: 120 samples during normal operation (no leaks) and 240 samples under leakage conditions, ensuring balanced representation across conditions. This procedure generated 360 samples per pressure condition, capturing AE signal variations corresponding to both normal and faulty states. The AE signals recorded during normal operation and leakage conditions are illustrated in Figures 8 and 9, offering visual insights into signal differences between these states. A detailed summary of the collected data, including variations across normal and leak states under different pressure levels, is presented in Table 4. This dataset provides a comprehensive foundation for fault detection and classification analysis in pipeline monitoring systems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multirow{2}{*}{**Pressure of Fluid (Bars)**} & \multirow{2}{*}{**Leak Size (mm)**} & \multirow{2}{*}{**Time (s)**} & \multicolumn{2}{c}{**Number of Samples**} \\ \cline{5-5}  & & & & **Non-Leak (Normal)** & **Leak** \\ \hline Water & 13 & 1.0 & 360 & 120 & 240 \\ \hline Gas & 18 & 1.0 & 360 & 120 & 240 \\ \hline Water & 7 & 0.7 & 360 & 120 & 240 \\ \hline Gas & 7 & 0.5 & 360 & 120 & 240 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data set description.