A review of leak detection methods based on pressure waves in gas pipelines

被引:6
|
作者
Zhao, Linkun [1 ]
Cao, Zheng [1 ]
Deng, Jianqiang [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Chem Engn & Technol, Xian 710049, Peoples R China
基金
中国国家自然科学基金;
关键词
Pressure wave; Leak; Gas pipeline; Detection; GALVANIZED STEEL PIPE; ACOUSTIC-EMISSION; LOCATION METHOD; NOISE-REDUCTION; STRAIN SENSOR; SIGNAL; LOCALIZATION; EXTRACTION; DESIGN; FLOW;
D O I
10.1016/j.measurement.2024.115062
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The integrity of gas pipelines is important for fuel delivery, but pipeline leaks frequently occur for a variety of reasons. As a result, pipeline leak detection for gas pipelines is now an important issue of concern. Currently, there is an absence of review regarding the development of pressure wave-based method for detecting leaks in gas pipelines. Therefore, this paper surveys the published research articles in related fields in recent decades. The principles and applications of leak detection based on pressure wave are categorized. Different signal analysis and machine learning techniques utilized in the detection of leakage are surveyed. In addition, the localization range, localization capability (size of the leak), and localization error of different methods are investigated in detail. The advantages, limitations, and performance of different detection methods are discussed, and potential future directions are proposed. This will be beneficial to experts and engineers working in this area of expertise.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Leak State Detection and Size Identification for Fluid Pipelines with a Novel Acoustic Emission Intensity Index and Random Forest
    Nguyen, Tuan-Khai
    Ahmad, Zahoor
    Kim, Jong-Myon
    Lo, Wai Lun
    SENSORS, 2023, 23 (22)
  • [42] Experimental Validation of Leak and Water-Ingression Detection in Low-Pressure Gas Pipeline Using Pressure and Flow Measurements
    Ravula, Sugunakar Reddy
    Narasimman, Srivathsan Chakaravarthi
    Wang, Libo
    Ukil, Abhisek
    IEEE SENSORS JOURNAL, 2017, 17 (20) : 6734 - 6742
  • [43] Experimental validation of gas leak detection in screw thread connections of galvanized pipe based on acoustic emission and neural network
    Gong, Chenyang
    Li, Suzhen
    Song, Yanjue
    STRUCTURAL CONTROL & HEALTH MONITORING, 2020, 27 (01)
  • [44] A novel hybrid technique for leak detection and location in straight pipelines
    Zhang, Tiantian
    Tan, Yufei
    Zhang, Xuedan
    Zhao, Jinhui
    JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2015, 35 : 157 - 168
  • [45] Experiment Study on Small Leak Detection and Diagnosis for Propulsion System Pipelines of Sounding Rocket
    Wang, Shaofeng
    Dong, Lili
    Wang, Jianguo
    Wang, Hailing
    Ji, Chunsheng
    Hong, Jun
    IEEE ACCESS, 2020, 8 : 8743 - 8753
  • [46] Systematic Review on Research Trends on Sensor-Based Leak Detection Methods in Water Distribution Systems
    Shirajuddin, Talhah Mohamad
    Muhammad, Nur Shazwani
    Abdullah, Jazuri
    JURNAL KEJURUTERAAN, 2022, 34 (02): : 201 - 209
  • [47] An unsupervised leak detection method with aggregating prediction and reconstruction along projection pathway for natural gas gathering pipelines
    Zhang, Hao
    Zuo, Zhonglin
    Li, Zheng
    Ma, Li
    Liang, Shan
    PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2023, 179 : 275 - 289
  • [48] Leak Detection and Location of Pipelines Based on LMD and Least Squares Twin Support Vector Machine
    Liang, Xianming
    Li, Ping
    Hu, Zhiyong
    Ren, Hong
    li, Yan
    IEEE ACCESS, 2017, 5 : 8659 - 8668
  • [49] A straightforward strategy for leak localization in two-phase gas pipelines
    Figueiredo, Aline Barbosa
    Baptista, Renan Martins
    Rachid, Felipe Bastos de Freitas
    Bodstein, Gustavo Cesar Rachid
    JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2021, 94
  • [50] A comparison between leak location methods based on the negative pressure wave
    Delgado, Mijail Romero
    Mendoza, Ofelia Begovich
    2017 14TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTING SCIENCE AND AUTOMATIC CONTROL (CCE), 2017,