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脉冲涡流技术对包覆层管道缺陷检测 被引量:4

Pulse Eddy Current Technology for Detection of Coating Pipe Defects
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摘要 包覆层下管道腐蚀引起局部减薄对工业造成重大安全隐患,因此评估带包覆层管道缺陷极为重要。研究主要目的找到一种有效且易于提取的信号特征以评估管道缺陷,利用Comsol仿真软件建立有限元模型,分析了脉冲涡流时域信号特征,提取检出差分电压峰值特征量,分析了差分电压峰值和试件缺陷之间的关系。同时通过20#碳钢管道实验验证,实验结果表明:在时域上,差分电压峰值随着外壁缺陷深度和宽度增加而增加,差分电压峰值随着内壁缺陷深度增加而减小,进而利用差分电压峰值评估铁磁性材料缺陷,该特征量对解决包覆层下管道缺陷评估具有广泛的工程应用价值。 Corrosion of the pipe under the coating layer causes a major safety hazard to the industry due to local thinning.Therefore,it is extremely important to evaluate the defects of the pipe with the coating layer.The main purpose of the research is to find an effective and easy-to-extract signal feature to evaluate pipeline defects,use Comsol simulation software to establish a finite element model,analyze the pulse eddy current signal characteristics in the time domain,extract and detect the differential voltage peak feature quantity,and analyze the differential voltage peak value The relationship between the defect and the test piece.At the same time,it is verified by the 20#carbon steel pipeline experiment.The experimental results show that in the time domain,the differential voltage peak value increases with the increase of the depth and width of the outer wall defect,and the differential voltage peak value decreases with the increase of the inner wall defect depth,and then the differential voltage is used Peak value evaluation of ferromagnetic material defects,this characteristic quantity has a wide range of engineering application value for solving the evaluation of pipeline defects under the coating.
作者 原鹏 魏治杰 王恪典 李鹏 Yuan Peng;Wei Zhijie;Wang Kedian;Li Peng(College of Mechanical Engineering,Xinjiang University,Urumqi Xinjiang 830047,China;College of Mechanical Engineering,Xi’an Jiaotong University,Xi'an 710049,China;Xinjiang Uygur Autonomous Region Special Equipment Inspection and Research Institute,Urumqi Xinjiang 830047,China)
出处 《机械设计与制造》 北大核心 2023年第9期55-58,63,共5页 Machinery Design & Manufacture
基金 自治区区域协同创新专项(2019E0239)。
关键词 脉冲涡流 缺陷检测 铁磁性材料 差分电压峰值 差分信号 时域分析 Pulse Eddy Current Defect Detection Ferromagnetic Material Differential Voltage Peak Value Differential Signal Time Domain Analysis
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