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Location of coating defects and assessment of level of cathodic protection on underground pipelines using AC impedance, deterministic and non-deterministic models.

机译:使用交流阻抗,确定性和非确定性模型对地下管道的涂层缺陷进行定位并评估阴极保护水平。

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摘要

A methodology for detecting and locating defects or discontinuities on the outside covering of coated metal underground pipelines subjected to cathodic protection has been addressed. On the basis of wide range AC impedance signals for various frequencies applied to a steel-coated pipeline system and by measuring its corresponding transfer function under several laboratory simulation scenarios, a physical laboratory setup of an underground cathodic-protected, coated pipeline was built. This model included different variables and elements that exist under real conditions, such as soil resistivity, soil chemical composition, defect (holiday) location in the pipeline covering, defect area and geometry, and level of cathodic protection.; The AC impedance data obtained under different working conditions were used to fit an electrical transmission line model. This model was then used as a tool to fit the impedance signal for different experimental conditions and to establish trends in the impedance behavior without the necessity of further experimental work. However, due to the chaotic nature of the transfer function response of this system under several conditions, it is believed that non-deterministic models based on pattern recognition algorithms are suitable for field condition analysis. A non-deterministic approach was used for experimental analysis by applying an artificial neural network (ANN) algorithm based on classification analysis capable of studying the pipeline system and differentiating the variables that can change impedance conditions. These variables include level of cathodic protection, location of discontinuities (holidays), and severity of corrosion. This work demonstrated a proof-of-concept for a well-known technique and a novel algorithm capable of classifying impedance data for experimental results to predict the exact location of the active holidays and defects on the buried pipelines. Laboratory findings from this procedure are promising, and efforts to develop it for field conditions should continue.
机译:已经提出了一种用于检测和定位经受阴极保护的涂覆金属地下管道的外部覆盖物上的缺陷或不连续性的方法。基于应用于钢涂层管道系统的各种频率的宽范围交流阻抗信号,并通过在几种实验室模拟情况下测量其相应的传递函数,建立了地下阴极保护的涂层管道的物理实验室设置。该模型包括在实际条件下存在的不同变量和元素,例如土壤电阻率,土壤化学成分,管道覆盖物中的缺陷(假期)位置,缺陷区域和几何形状以及阴极保护水平。在不同工作条件下获得的交流阻抗数据用于拟合电传输线模型。然后将该模型用作一种工具,以适应不同实验条件下的阻抗信号,并建立阻抗行为的趋势,而无需进一步的实验工作。但是,由于该系统在几个条件下传递函数响应的混沌性质,可以认为基于模式识别算法的非确定性模型适用于现场条件分析。一种非确定性方法用于实验分析,方法是应用基于分类分析的人工神经网络(ANN)算法,该算法能够研究管道系统并区分可改变阻抗条件的变量。这些变量包括阴极保护级别,不连续位置(假期)和腐蚀严重程度。这项工作证明了一项众所周知的技术的概念证明和一种新颖的算法,该算法能够对用于实验结果的阻抗数据进行分类,以预测活动的假期和地下管道上的缺陷的确切位置。该程序的实验室发现是有希望的,应继续努力以在现场条件下进行开发。

著录项

  • 作者

    Castaneda-Lopez, Homero.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Materials Science.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 107 p.
  • 总页数 107
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 工程材料学;
  • 关键词

  • 入库时间 2022-08-17 11:47:10

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