## 4 Laboratory Experiments  
### Experimental Setup and Data Collection  
The experimental setup consists of two parts: a pressurized piping system and a signal acquisition system, as shown in Figure 5. If the pipe gas pressure is too low, the energy of the leak-induced signal  
Figure 4: The MLPNN with input, hidden, and output layers.  
will be small and difficult to detect. Therefore, in the pressurized piping system, an air compressor (NH-5, Hanshin product) supplies 7 bar gas pressure to a 15m-long steel pipe (ASTM A 312 TP304) with a pipe wall thickness and external diameter of 2.8 mm and 33.4 mm, respectively. One end of the pipe was blocked, and the other end was connected to the air compressor via a hose. To avoid the air compressor vibration noise affecting the experimental results, the air compressor was placed far away from the piping system. The pipe was supported by a sponge material to reduce energy dissipation of the AE signal on the pipe wall to the ground. The diameter of the leakage orifice in the early stage of a pipeline leakage is usually small. To verify that the proposed method can locate a leak in this stage, a simulated leak orifice with a diameter of 0.3 mm was punched in the pipe wall. The simulated leak source, the AE sensor mounted on the pipe wall, an impulse hammer and the placement of two AE sensors along the pipe are shown in Figures 6-9, respectively. The pipe wall had been cleaned with sandpaper before the AE sensors were mounted with the magnetic hold downs and vacuum grease couplant.  
The signal acquisition system consists of two R15a sensors with a resonant frequency of 150 kHz, an impulse hammer (086C03), a data acquisition card (USB-5132, NI product) and a personal computer (PC) equipped with NI-SCOPE software. Since the sensitivity of the AE sensors is high in the frequency range between 50 kHz and 400 kHz, data were captured at a sampling rate of 800 kHz in the experiment. Analog to digital conversion was performed by the data acquisition card connected to the PC. Data were recorded into the PC for the signal processing and preprocessed by the NI-SCOPE software, including the preamplification and anti-aliasing filtering.  
Figure 8: An impulse hammer.  
Figure 7: The AE sensor mounted with the magnetic hold down and vacuum grease couplant.  
Figure 9: The placement of two AE sensors along the pipe.  
In the experiment, the training sets for MLPNN include the distances between one sensor and each impact location and the impulse response signals energy ratios between two sensor signals in different frequency bands. The impulse signal was measured by the sensor in the impact hammer, and the impulse response signals were measured by AE sensors when the pipe was tapped by the impact hammer, and the tapping locations were distributed along a straight line between the two AE sensors. To avoid the simulated leak orifice affecting the impulse response signal, the impact test was performed before the simulated leak orifice was punched. The test sets for MLPNN were the energy ratios of leakage-induced signals in different frequency bands. The leakage-induced signals were generated by the friction force between the jet of the leaking gas and the simulated leak orifice.