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针对油气长输管道外壁缺陷影响管道安全运行的问题,提出了一种基于动生涡流磁场的无损检测方法。借助ANSYS Electronics有限元分析软件分析了管道表面缺陷参数、检测速度与动生涡流磁场之间的关系,发现在不同管道表面缺陷参数下,动生涡流磁场会根据缺陷形状、尺寸的差异反馈出不同的磁感应强度信号,缺陷尺寸越大,信号反馈越强烈;随着检测速度的提高,动生涡流的磁感应强度逐渐增大,但整体变化趋势基本一致。根据工程实际研制了基于动生涡流磁场的管道无损检测试验装置,开展了试验研究,得到了与仿真相同的规律性结果,从而验证了该技术的可行性,表明动生涡流磁场无损检测方法能够对管道缺陷进行精准定位与辨别。 相似文献
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管道漏磁检测及其缺陷漏磁场的仿真技术研究具有十分重要的意义。在对各种漏磁场计算方法进行比较之后,选择了有限元法作为主要研究工具。叙述了漏磁检测的基本原理,介绍了漏磁管道检测装置的工作原理和基本结构,建立了管道漏磁检测中缺陷漏磁场计算的三维有限元模型,并以此为基础分别研究了缺陷漏磁信号特点、缺陷的几何尺寸与漏磁信号的关系、以及材料壁厚等对漏磁信号的影响等问题。通过对多磁化单元结构进行有限元模拟和试验仿真,发现多个磁化单元会造成磁场的叠加,磁化单元数量的增加会使缺陷处漏磁场增强,并且中间磁化单位的增加量要大于两侧。缺陷的几何尺寸影响漏磁场的分布,在一定缺陷直径范围内,缺陷深度与漏磁场信号强度呈近似线性关系。无论被测管道壁厚如何变化,相同几何参数的缺陷漏磁场轴向分量变化趋势仍然相同。适当选取提离值,将有助于获得良好的漏磁场信号。 相似文献
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传统的漏磁检测是经磁化后测量试样表面漏磁场的垂直分量再对拾取的信号量进行分析研究的方法,然而这种仅通过单一垂直分量特性来判定缺陷的方法易出现漏检、误判。针对该方法的不足,本研究采用了一种在测量漏磁场垂直分量的同时又提取并分析水平分量特性的二维检测方法,考虑到水平分量易受干扰信号影响,运用了矢量合成法提取水平分量,通过联合利用漏磁场的垂直分量存在过零点和水平分量具有最大值特性的方法来判断缺陷,并选取多种标准与自然试样进行试验研究。试验结果表明:采用漏磁二维检测可有效实施铁磁构件的缺陷检测,提高检测可靠性,可望有很好的实际工程应用前景。 相似文献
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A method is proposed for sizing of 3-D surface breaking flaws with complex profiles. The method can be used for either magnetic materials or conductive materials when a magnetic field or electric current are distributed homogeneously inside the sample. The distribution of the intensity of the leakage magnetic field or electric field has to be measured at a constant distance above the surface of the sample in a direction perpendicular to the long axis of the flaw. The method is based on a dipole model of the flaw which predicts the intensity of the leakage field and on minimisation of the error between the measured distribution of the intensity of the leakage field and the corresponding theoretical distribution which changes with the size of the flaw. Computations are made for sizing of flaws with different shapes and sizes while it is assumed that the density of the charge at the walls of the flaw can vary. It is shown that the method allows fast distinguishing of the shape of a particular flaw where the sample contains several flaws with different shapes and the sizes of these flaws are known. Fast determination of the size of a particular flaw is achieved when the sample contains flaws with the same shape and different but known sizes. Also, the size of the flaw and the density of the charge at the walls of the flaw could be computed precisely, independently of the initial approximation of the minimisation procedure, when the shape of the flaw is known. The speed of the minimisation process increases with the decrease of the measurement distance from the surface of the flaw. Experimental requirements for precise flaw sizing are discussed. 相似文献
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For quantitative flaw characterization in steam generator tubes, inversion of eddy current testing (ECT) signals in an automated fashion is strongly desired. In this paper, we report our effort to develop a systematic approach for flaw characterization in tubes by the novel combination of neural networks and finite element modeling.Specifically, the finite element model that can predict ECT signals from axisymmetric flaws in tubes was developed, and its accuracy was verified experimentally. Using this model, an abundant synthetic database with 400 ECT signals generated from 200 axisymmetric machined grooves in four types has been constructed with two test frequencies per flaw.For the automated inversion of ECT signals, a total of 22 features have been extracted from each flaw. Then, a set of 10 features has been selected for flaw classification, while the other set of 10 features for flaw sizing. For the determination of the flaw type and the flaw size parameters, we have proposed an intelligent flaw characterization system that adopts two different paradigms of neural networks: probabilistic neural networks for flaw classification and back propagation neural networks for flaw sizing.The performance of this system has been investigated using the synthetic ECT signals in the database. The excellent performance presented here, even though it has been obtained from synthetic flaws representing machined grooves in tubes, demonstrates the high potential of this system to serve as a robust tool for practical flaw characterization in tubes. 相似文献
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矩形缺陷的交变漏磁检测信号分析方法 总被引:1,自引:0,他引:1
利用ANSYS仿真软件,首先对交变漏磁检测中影响矩形缺陷信号的各项因素进行了分析,并以此为基础,提出了以矩形缺陷为基本单元的信号分析方法。利用该方法分析了截面为梯形和圆形,及其它任意形状的直线裂纹的检测信号特点,同时也分析了圆弧形裂纹以及其他任意形状的非直线裂纹的检测信号特点,并对上述不同类型缺陷的识别进行了详细的论述。 相似文献
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依据缺陷在螺纹区域的不同位置,将缺陷分为三类,分析了每类缺陷检测信号的特点,提出了识别判据,最后通过对膨胀螺钉上的该三类缺陷的检测,验证了识别模型的正确性。另外,对六角头螺栓结构根部缺陷的检测进行了试验研究,利用测量信号完成了缺陷的检测。 相似文献
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超声无损检测已被广泛用来检测材料内部的缺陷,然而对缺陷性质的识别始终是检测的难点,为此研究了一种基于超声信号和图像融合的焊缝缺陷识别新方法.该方法充分利用检测数据,通过对缺陷回波信号特征与缺陷形态特征的数据融合,实现了焊缝缺陷的有效识别.利用自主研制的超声成像手动检测系统对含有气孔、夹渣、裂纹、未焊透和未熔合五类典型焊接缺陷的焊件进行了检测,分别提取缺陷的超声回波信号特征和缺陷图像的形态特征,构建神经网络实现超声信号和图像特征的数据融合.结果表明,该方法实现了多类缺陷的识别,提高了缺陷识别率,有助于焊缝质量评定. 相似文献
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Evaluation of echo features of ultrasonic flaws and its intelligent pattern recognition 总被引:1,自引:0,他引:1
In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted from each echo samples.A method which is based on the statistical hypothesis testing and used for feature evaluation and optimum subset selection was explored Thus. the dimensionality reduction of feature space was brought out, and simultaneously, the amount of calculation was decreased. An intelligent pattern classifier with B-P type neural network was constructed which was characterized by high speed and accuracy for learning. Using a half of total samples as training set and others as testing set, the learning efficiency and the classification ability of network model were studied. The results of experiment showed that the learning rate of different training samples was about 100%. The results of recognition was satisfactory when the optimum feature subset was taken as the sample's feat 相似文献
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石油管道的高速检测与缺陷识别 总被引:10,自引:3,他引:7
漏磁探伤法结合磁通量检测法可检出管全内、外壁及内部各种缺陷,描述检测图形曲线。通过长期检测数据积累,将有缺陷的和经过解剖对比的石油管道图形曲线资料输入计算机作为缺陷类型模板。检测中由计算机分析缺陷信号,与反比较,识别、分选出对油井有严重危害的管道。 相似文献