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摘 要：漏磁检测是在管道内检测中应用最广泛的一种无损检测技术，检测数据量化与分析是气难点。在技术方面针对课题重点研究的关键技术开展了一系列研究，提出了油气管道漏磁检测数据的分类和量化方法，并基于此研发出一套漏磁检测数据分析软件。漏磁检测中缺陷量化困难的原因在于缺陷的形态对漏磁场的形态有复杂的非线性的影响，继而影响对漏磁信号的定量解释，因此，根据缺陷的开口形状将缺陷进行分类，对于实现将其准确量化是十分必要的。再者，由于实际检测条件的限制，往往只能通过空间离散的漏磁感应强度信号的一维分量推算缺陷的三维形态，这本身不适合使用精确的数学或者统计模型加以描述。使用神经网络对缺陷进行量化，是漏磁检测缺陷量化领域近20年来的一个研究热点。根据课题研究内容以及检测器设计指标，提出了一种基于改进径向基函数网络的量化算法，它以缺陷漏磁场信号的特征量为输入，输出向量为缺陷的三维外形参数。径向基函数网络是一种局部最佳逼近网络，但漏磁检测中漏磁感应强度信号与缺陷外形之间强烈的非线性关系，往往更要求所选用的网络能够识别两者间的内在联系，并使得面对新的数据时仍有合理的量化结果。为此，对径向基函数网络做出基于泛化能力优化的改进，提出新的评价函数，并采用能够迅速适应新样本的在线学习算法，实验验证表明，的确能大幅提高网络的泛化能力。在实际工程检测管道中，多缺陷聚集会明显影响漏磁场的形态，轴向槽缺陷漏磁场与两个坑状缺陷信号波形极为相似，缓变缺陷漏磁场信号变化趋势较小，这对定量漏磁检测的实用化是不容忽视的问题。讨论了不同类型缺陷漏磁场形态和强度的影响，并测试了量化神经网络对缺陷间隔变化的适应能力。研究以分类和量化算法为核心，研发一套漏磁检测数据分析系统。该系统配合内检测器已项目中投入测试，对牵拉实验数据分析的结果验证了所提出算法的确具有优秀的量化性能。
关键词：漏磁检测 缺陷分类 缺陷量化 多缺陷聚集 数据分析系统
Abstract：The magnetic flux leakage（MFL） is the most generalized method for in-pipe inspection. A method of classification and quantification of defects in MFL inspection is proposed， and a data analysis system is developed based on this method. The pattern of magnetic flux leakage has a complex non-linear relationship with the shape of defects， which makes it a difficult problem to make quantitative analysis to the magnetic flux leaked.Furthermore， in reality testing conditions， usually only the component in one direction is detected for quantification. Such problems do not adapt to accurate mathematical models. Utilizing neural network as a quantification method has become a focus in MFL inspection during the last 20 years. A method of quantification based on modified radial base function neural network （RBFNN） is proposed. RBFNN promises locally optimal approximation， but the non-linear relationship between magnetic flux pattern and the defect shape requires a strong capability to recognize their inner connection， to better deal with generalized samples.Anon-line trainingalgorithm to determine the number of nodes in hidden layer is proposed， and new merit function based on optimized generalization is employed to train the central vectors and widths. Both of them， verified by experiments， can greatly enhanced the generalized capability of RBFNN. Corrosions usually appear as multi-defect assemblies in pipelines. The relationship between magnetic flux leakage and the pattern of multi-defect assembly is discussed. And different neural network models are employed to solve the inverse problem for multi-defect assembly. Based on the methods stated above， a data analysis expert system is developed. This system works coordinating with in-line inspector and is tested in a submerged pipeline in-service testing project. Results prove that the modified methods gives accurate predicts to a wide range of defects.
Key Words：Magnetic Flux Leakage Inspection；Classification of Defects；Quantification of Defects；Multi-defect Assembly；Data Analysis System
阅读全文链接（需实名注册）：http：//www.nstrs.cn/xiangxiBG.aspx？id=65390&flag=1
关键词：漏磁检测 缺陷分类 缺陷量化 多缺陷聚集 数据分析系统
Abstract：The magnetic flux leakage（MFL） is the most generalized method for in-pipe inspection. A method of classification and quantification of defects in MFL inspection is proposed， and a data analysis system is developed based on this method. The pattern of magnetic flux leakage has a complex non-linear relationship with the shape of defects， which makes it a difficult problem to make quantitative analysis to the magnetic flux leaked.Furthermore， in reality testing conditions， usually only the component in one direction is detected for quantification. Such problems do not adapt to accurate mathematical models. Utilizing neural network as a quantification method has become a focus in MFL inspection during the last 20 years. A method of quantification based on modified radial base function neural network （RBFNN） is proposed. RBFNN promises locally optimal approximation， but the non-linear relationship between magnetic flux pattern and the defect shape requires a strong capability to recognize their inner connection， to better deal with generalized samples.Anon-line trainingalgorithm to determine the number of nodes in hidden layer is proposed， and new merit function based on optimized generalization is employed to train the central vectors and widths. Both of them， verified by experiments， can greatly enhanced the generalized capability of RBFNN. Corrosions usually appear as multi-defect assemblies in pipelines. The relationship between magnetic flux leakage and the pattern of multi-defect assembly is discussed. And different neural network models are employed to solve the inverse problem for multi-defect assembly. Based on the methods stated above， a data analysis expert system is developed. This system works coordinating with in-line inspector and is tested in a submerged pipeline in-service testing project. Results prove that the modified methods gives accurate predicts to a wide range of defects.
Key Words：Magnetic Flux Leakage Inspection；Classification of Defects；Quantification of Defects；Multi-defect Assembly；Data Analysis System
阅读全文链接（需实名注册）：http：//www.nstrs.cn/xiangxiBG.aspx？id=65390&flag=1