Application of NL-Net in natural gas pipeline leak detection system based on DFB-FL vibration sensor

When a small hole leakage occurs in the natural gas pipeline, it is difficult to be detected. To detect small hole leaks in natural gas pipelines more quickly, effective inspections need to be conducted on the natural gas pipeline. Therefore, this paper proposes a deep learning detection method based on a distributed feedback fiber laser (DFB-FL) vibration sensor, known as NL-Net. The DFB-FL vibration sensor has high strain resolution and is highly sensitive to acoustic signals, making it suitable for capturing pipeline leak signals. NL-Net consists of multiple local feature extraction blocks (LFLBs), which can deeply analyze the features in the signal and classify leaks of different sizes. Experimental results demonstrate that NL-Net achieves a classification accuracy of up to 97.4% for different leak sizes, with a testing response time of only around 10ms and rapid convergence. The combination of NL-Net with the DFB-FL vibration sensor-based natural gas pipeline leak system enhances its practical applicability, contributing to the development of a more efficient and reliable natural gas pipeline infrastructure. This integration offers effective analytical algorithm support for future research endeavors focused on improving the safety and efficiency of natural gas pipeline systems.