Wei Haonan, Liu Yi, Hao Zejia
State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.
China South-to-North Water Diversion Middle Route Corporation Limited, Beijing 100038, China.
Sensors (Basel). 2025 Aug 7;25(15):4859. doi: 10.3390/s25154859.
Distributed acoustic sensing shows great potential for pipeline monitoring. However, internally deployed and unfixed sensing cables are highly susceptible to disturbances from water flow noise, severely challenging impact source localization. This study proposes a novel two-step method to address this. The first step employs Variational Mode Decomposition (VMD) combined with Short-Time Energy Entropy (STEE) for the adaptive extraction of impact signal from noisy data. STEE is introduced as a stable metric to quantify signal impulsiveness and guides the selection of the relevant intrinsic mode function. The second step utilizes the Pruned Exact Linear Time (PELT) algorithm for accurate signal segmentation, followed by an unsupervised learning method combining Dynamic Time Warping (DTW) and clustering to identify the impact segment and precisely pick the arrival time based on shape similarity, overcoming the limitations of traditional pickers under conditions of complex noise. Field tests on an operational water pipeline validated the method, demonstrating the consistent localization of manual impacts with standard deviations typically between 1.4 m and 2.0 m, proving its efficacy under realistic noisy conditions. This approach offers a reliable framework for pipeline safety assessments under operational conditions.
分布式声学传感在管道监测方面显示出巨大潜力。然而，内部部署且未固定的传感电缆极易受到水流噪声干扰，这对冲击源定位构成了严峻挑战。本研究提出一种新颖的两步法来解决这一问题。第一步采用变分模态分解（VMD）结合短时能量熵（STEE）从噪声数据中自适应提取冲击信号。引入STEE作为一种稳定的度量来量化信号的脉冲性，并指导相关本征模态函数的选择。第二步利用精简精确线性时间（PELT）算法进行精确的信号分割，随后采用结合动态时间规整（DTW）和聚类的无监督学习方法来识别冲击段，并基于形状相似性精确选取到达时间，克服了传统拾取器在复杂噪声条件下的局限性。在一条运行中的输水管道上进行的现场测试验证了该方法，表明人工冲击的定位结果具有一致性，标准差通常在1.4米至2.0米之间，证明了其在实际噪声条件下的有效性。这种方法为运行条件下的管道安全评估提供了一个可靠的框架。
