管道缺陷漏磁检测的三维有限元仿真分析

为研究运输管道缺陷的漏磁（MFL）信号特征，采用ANSYS软件进行管道缺陷漏磁检测的三维有限元数值仿真。首先建立漏磁检测分析装置的数值模型，进行三维静态情况的数值计算，获得不同缺陷所产生的漏磁信号，然后将这些数据与试验结果进行比较分析。在此基础上，分析缺陷深度、缺陷直径以及传感器提离值对漏磁信号参数的影响。结果表明：磁通密度轴向分量By的幅值能够用于衡量缺陷的深度，可依据By的宽度和磁通密度径向分量Bx的峰峰间距来定量识别缺陷直径；传感器的提离值对缺陷漏磁场的影响很大，为使测得的漏磁信号可靠并能测得深度较小的缺陷，传感器的提离值应设定为1．5～3mm。

自相关分析在漏磁信号检测中的应用

漏磁检测(MFL)是目前常用的一种无损检测(NDE)方法,针对漏磁检测中漏磁信号微弱且噪声影响大的问题,提出采用自相关分析的方法处理漏磁信号,提高检测精度。通过理论推导验证自相关分析能够有效消除噪声干扰,并通过Matlab仿真证明其有效性。

基于灰度-梯度共生矩阵的焊缝缺陷聚类分析

为识别焊缝不同类型缺陷,以焊缝缺陷的漏磁检测(MFL)图像为研究对象,提出基于灰度-梯度共生矩阵(GGCM)和聚类分析的焊缝缺陷识别方法。将采集的三维MFL信号转换为二维灰度图像,利用GGCM直接提取焊缝3种状态下(焊缝无缺陷、焊道上分布圆柱体缺陷、焊道上分布矩形槽缺陷)MFL图像的特征信息。结合2种聚类算法,用层次聚类法选取评述焊缝图像的特征量,用k-均值聚类方法分析这些特征量,并以可视化图形显示聚类结果。结果表明:根据GGCM提取的特征量的聚类分析结果,焊缝典型缺陷的识别率大于96%。

Wavelet domain adaptive filtering algorithm for removing the seamless pipe noise contained in the magnetic flux leakage data

With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.