Pipeline Leaks Early Warning Based on Distributed Optical Fiber Sensing Technology
Muxin Jia, Zhaoxiong Sun, Chao Duan, Tao Tan, Xuan Du, Ye Tian, Yuxiang Luo, Jianzhong Zhang
Abstract
To address the problem of early warning of pipeline leakage, this letter proposes a pipeline leakage early warning method based on DAS. Through DAS monitoring the vibration signals induced by different degree of pipeline corrosion and blockage, using pattern recognition techniques, distributed early warning and location of pipeline leakage are realized. By conducting simulations and experimental analyses, the differences in vibration characteristics caused by four types of pipeline anomalies were validated. Combining wavelet packet energy ratios and the BP neural network, the average recognition rate for the five event classes (normal pipeline, 40% corrosion, 80% corrosion, 40% blockage, 70% blockage) was 95.39% based on 3910 samples. The results demonstrate that the proposed method can effectively achieve the early warning of pipeline leakage.