确保船舶管道系统安全运行是一项关键任务，特别是现代的大型运输船舶，这些船舶负责输送燃油、压缩空气等化学性质活跃的物质，一旦船舶管道发生泄漏，不仅会导致资源浪费，甚至可能引发安全事故。为了实现利用传感器阵列对船舶管道泄漏进行准确的定位，本文提出一种结合变分模态分解（VMD）和广义互相关（GCC）的泄漏定位方法。考虑到船舶在海上航行时复杂的环境噪声，研究首先应用VMD对从各个传感器获得的泄漏信号进行多重分解，随后基于互相关系数自适应地选取主要的固有模态函数（IMF）分量，并消除噪声成分。此外，本文考虑到广义互相关权函数的特性，进一步提出一种改进的权函数，以纳入信噪比对时延估计精度的影响。以五元十字形传感器阵列为例，本文详细阐述了声源定位的计算方法。通过实施管道泄漏实验，研究结果验证了所提方法在不同工况下都能实现鲁棒且精确的时延估计，从而准确地定位管道泄漏。
Ensuring the safe operation of marine pipeline systems is a critical task, especially for modern large-scare transportation ships, which are responsible for transporting chemically active substances such as fuel oil and compressed air, and once a leak occurs in a ship pipeline, it will not only lead to a waste of resources, but may also even lead to safety accidents. In order to accurately locate leaks in ship pipelines using sensor arrays, this study proposes a leak localization method that combines Variational Mode Decomposition (VMD) with Generalized Cross-Correlation (GCC). Considering the complex environmental noise encountered by ships during maritime navigation, the research first applies VMD to decompose the leak signals, then adaptively selects the principal Intrinsic Mode Function (IMF) components based on the cross-correlation coefficient, and eliminates noise components. Moreover, considering the characteristics of the weighting function, this study further proposes an improved weighting function to incorporate the impact of signal-to-noise ratio on the accuracy of time delay estimation. Taking a cross-shaped sensor array as an example, this paper elaborates in detail the computational method for acoustic source localization. Through conducting pipeline leak experiments, the research results verify that the proposed method can achieve robust and precise time delay estimation under different working conditions, thereby accurately locating pipeline leaks.
2025,47(6): 55-61 收稿日期：2024-5-29
DOI：10.3404/j.issn.1672-7649.2025.06.009
分类号：U672
作者简介：夏丹（1982 – ），女，博士，工程师，研究方向为船舶管路系统异音监测
参考文献：
[1] MENG L Y, LI Y X, WANG W C, et al. Experimental study on leak detection and location for gas pipeline based on acoustic method[J]. Journal of Loss Prevention in the Process Industries, 2012, 25(1): 90-102.
[2] 王效东, 黄坤, 朱小华, 等. 油气管道泄漏检测技术发展现状[J]. 管道技术与设备, 2008(1): 24-26.
WANG X D, HUANG K, ZHU X H, et al. Present state of the development of oil-gas pipelines leak-detection technologies[J]. Pipeline Technique and Equipment, 2008(1): 24-26.
[3] 曹智敏, 胡斌, 李志农, 等. 输气管道泄漏声波识别和定位方法研究现状[J]. 无损检测, 2023, 45(3): 49–57.
CAO Z M, HU B, LI Z N, et al. Research status of acoustic wave identification and location methods for gas pipeline leakage[J]. Nondestructive Testing, 2023, 45(3): 49–57.
[4] FAN H, TARIQ S, ZAYED T. Acoustic leak detection approaches for water pipelines[J]. Automation in Construction, 2022, 138: 104226.
[5] MURVAY P S, SILEA I. A survey on gas leak detection and localization techniques[J]. Journal of Loss Prevention in the Process Indust-ries, 2012, 25(6): 966-973.
[6] NING F L, SONG J H, HU J L, et al. Sound source localization of non-synchronous measurements beamforming with block Hermitian matrix completion[J]. Mechanical Systems and Signal Processing, 2021, 147: 107118.
[7] 吴晓, 靳世久, 李一博, 等. 基于麦克风阵列声音信号定位方法的研究[J]. 传感技术学报, 2010, 23(5): 682–686.
WU X, JIN S J, LI Y B, et al. Sound source localization based on microphone arrays[J]. Chinese Journal of Sensors and Actuators, 2010, 23(5): 682–686.
[8] 崔玮玮, 曹志刚, 魏建强. 声源定位中的时延估计技术[J]. 数据采集与处理, 2007(1): 90-99.
CUI W W, CAO Z G, WEI J Q. Time delay estimation techniques in source location[J]. Journal of Data Acquisition & Processing, 2007(1): 90-99.
[9] 郑晓亮, 王强, 薛生, 等. 基于延迟求和的输气管道泄漏声波定位方法[J]. 仪器仪表学报, 2019, 40(11): 241–249.
ZHENG X L, WANG Q, XUE S, et al. Leakage localization for gas pipelines based on delay-and-sum using acoustic signal[J]. Chinese Journal of Scientific Instrument, 2019, 40(11): 241–249.
[10] 郑晓亮, 廖冉, 王强. 埋地输气管道泄漏振动声源双阵列定位方法[J]. 油气储运, 2023, 42(6): 641–652.
ZHENG X L, LIAO R, WANG Q. Dual-array localization of vibroacoustic source for leaks in buried gas pipelines[J]. Oil & Gas Storage and Transportation, 2023, 42(6): 641–652.
[11] 张梅, 张双双, 袁宏永, 等. 燃气管道泄漏的次声源定位算法研究[J]. 电子测量与仪器学报, 2020, 34(3): 187–194.
ZHANG M, ZHANG S S, YUAN H Y, et al. Research on infrasound source location algorithm of gas pipeline leakage[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(3): 187–194.
[12] 王强, 郑晓亮, 薛生, 等. 输气管道泄漏的波达时差交叉定位方法[J]. 应用声学, 2020, 39(3): 472–480.
WANG Q, ZHENG X L, XUE S, et al. Leakage localization method for gas pipelines using cross localization based on time difference of arrival[J]. Journal of Applied Acoustics, 2020, 39(3): 472–480.
[13] DRAGOMIRETSKIY K, ZOSSO D. Variati-onal Mode Decomposition[J]. IEEE Transactions on Si-gnal Processing, 2014, 62(3): 531-544.
[14] BOYD S. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers[J]. Foundations and Trends® in Machine Learning, 2010, 3(1): 1-122.
[15] LEE RODGERS J, NICEWANDER W A. Thirteen Ways to Look at the Correlation Coefficient[J]. The American Statistician, 1988, 42(1): 59-66.
[16] 路炜, 文玉梅. 供水管道泄漏声信号及其传播特性[J]. 声学技术, 2007, 26(5): 871-876.
LU W, WEN Y M. Leakage noise and its propagation in water pipeline[J]. Technical Acoustics, 2007, 26(5): 871-876.
[17] 杨进, 文玉梅, 李平. 基于相关分析和近似熵的管道泄漏声信号特征提取及辨识方法[J]. 仪器仪表学报, 2009, 30(2): 272-279.
YANG J, WEN Y M, LI P. Feature extraction and identification of leak acoustic signal in water distribution pipelines using correlation analysis and approximate entropy[J]. Chinese Journal of Scientific Instrument, 2009, 30(2): 272-279.
[18] CHATFIELD C. The analysis of time series: an introduction[M]. 4th ed. London New York Tokyo, Chapman and Hall, 1989.
[19] GAO Y, BRENNAN M J, JOSEPH P F. A comparison of time delay estimators for the detection of leak noise signals in plastic water distribution pipes[J]. Journal of Sound and Vibration, 2006, 292(3-5): 552-570.
