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[1]张丽珍,王文奥.天然气管道阀门微小泄漏声发射检测及缺陷识别方法[J].油气储运,2024,43(06):656-664.[doi:10.6047/j.issn.1000-8241.2024.06.007]
　ZHANG Lizhen,WANG Wen&apos,ao.Acoustic emission detection and defect identification method for micro-leakage in natural gas pipeline valves[J].Oil & Gas Storage and Transportation,2024,43(06):656-664.[doi:10.6047/j.issn.1000-8241.2024.06.007]
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天然气管道阀门微小泄漏声发射检测及缺陷识别方法

《油气储运》[ISSN:1000-8241/CN:13-1093/TE] 卷: 43 期数: 2024年06期 页码: 656-664 栏目: 检测与完整性 出版日期: 2024-06-25
Title:
Acoustic emission detection and defect identification method for micro-leakage in natural gas pipeline valves
文章编号:
1000-8241（2024）06-0656-09
作者:
张丽珍1; 王文奥2
1.重庆安全技术职业学院安全监督管理系; 2.中国石油大学（华东）机电工程学院
Author(s):
ZHANG Lizhen1; WANG Wen'ao2
1.Department of Safety Supervision and Management, Chongqing Vocational Institute of Safety Technology; 2.College of Mechanical and Electronic Engineering, China University of Petroleum (East China）
关键词:
天然气管道; 阀门; 微小泄漏; 缺陷; 声发射技术; 特征提取; 主成分分析方法; 聚类分析
Keywords:
natural gas pipeline;  valve;  micro-leakage;  defect;  acoustic emission technology;  feature extraction;  principal component analysis;  clustering analysis
分类号:
TE88
DOI:
10.6047/j.issn.1000-8241.2024.06.007
文献标志码:
A
摘要:
【目的】阀门是保障天然气管道系统稳定运行的重要元件，为了实现天然气阀门早期泄漏的实时监测及缺陷识别，亟需建立一种可检测并识别不同微小泄漏缺陷的有效方法。【方法】利用自主搭建的阀门微小泄漏声发射检测实验装置，设置0.2MPa、0.4MPa及0.6MPa共3种压力，采用声发射检测方法采集了5种阀门状态（健康阀门、阀杆腐蚀外漏缺陷、阀门封闭不严内漏缺陷、阀芯划伤内漏缺陷及法兰垫片破损外漏缺陷）的声发射信号，并得到其时频域特征矩阵。通过主成分分析方法（Principal Component Analysis,PCA）将复杂、冗余的时频域特征矩阵转化为二维、三维特征因子矩阵，计算得到各个特征因子矩阵的最佳簇数，并通过K-means、K-medoids聚类算法对二维、三维特征因子矩阵进行聚类分析。【结果】对于较低压力（0.2MPa）下天然气阀门微小泄漏缺陷的识别，三维PCA聚类效果达到97.8％，优于二维PCA；随着压力增至0.6MPa，二维PCA聚类效果不断优化，并逐渐超过三维PCA。在聚类方法评价中，K-medoids聚类算法的稳定程度优于K-means聚类算法，且K-medoids聚类算法的运算效率更有优势。【结论】基于声发射技术+特征提取+PCA+聚类算法的声发射信号分类处理方法，提高了阀门微小泄漏检测的准确率与稳定性，实现了天然气管道阀门缺陷的分类识别，可为天然气管道系统的安全运行提供检测手段及保障措施。（图7，表2，参26）
Abstract:
[Objective] The pivotal role valves play in ensuring the stable operation of natural gas pipeline systems necessitates real-time monitoring and defect identification to eliminate early leakage in natural gas valves. To this end, it is imperative to develop an effective method for detecting and identifying micro-leakage defects. [Methods] An acoustic emission detection experimental setup was initially established targeting valve micro-leakage, with three pressure settings of 0.2 MPa, 0.4 MPa, and 0.6 MPa. This self-developed setup was utilized to capture the acoustic emission signals across five valve conditions (healthy state, external leakage due to valve stem corrosion, internal leakage due to untightness in valve closure, internal leakage due to valve core scratch, and external leakage due to flange gasket damage) through the acoustic emission detection method. The collected signals were then used to create time-frequency domain feature matrices. These intricate and redundant time-frequency domain feature matrices were transformed into two-dimensional and three-dimensional feature factor matrices utilizing Principal Component Analysis (PCA). The optimal number of clusters for each feature factor matrix was determined through subsequent calculations. The two-dimensional and three-dimensional feature factor matrices were further analyzed using K-means and K-medoids clustering methods. [Results] For the identification of micro-leakage defects of natural gas valves under low pressure conditions (0.2 MPa), the clustering effect of 3D PCA reached 97.8%, surpassing that of 2D PCA. As the pressure was gradually raised to 0.6 MPa, the clustering effect of 2D PCA continued to improve, eventually surpassing that of 3D PCA. In the evaluation of clustering methods, it was observed that the K-medoids method exhibited greater stability and higher operational efficiency compared to the K-means method. [Conclusion] The classified processing method for acoustic emission signals, based on acoustic emission technology, feature extraction, PCA, and clustering algorithms, can enhance the accuracy and stability of valve micro-leakage detection. Moreover, it enables the classified identification of defects in natural gas pipeline valves. This approach not only offers monitoring means but also establishes safeguard measures crucial for ensuring the secure operation of natural gas pipeline systems. (7 Figures, 2 Tables, 26 References)

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备注/Memo

张丽珍，女，1989年生，讲师，2016年硕士毕业于中国石油大学（华东）安全科学与工程专业，现主要从事阀门及管道泄漏方向的研究工作。地址：重庆市万州区百安坝安庆路583号，404100。电话：17764801079。Email：keyangongzuo521@163.com
基金项目：重庆市教育委员会科学技术研究项目“基于声发射技术的阀门内漏状态识别及量化分析”，KJQN202104703；重庆市教育委员会科学技术研究项目“基于声发射技术的管道泄漏状态识别研究”，KJQN202204704。
· Received: 2023-05-29 · Revised: 2023-07-09 · Online: 2024-03-29

更新日期/Last Update: 2024-06-25