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Advanced studies on eddy current testing using computational intelligence related to electromagnetic inverse problems

利用与电磁反演问题相关的计算智能进行涡流检测的高级研究

基本信息

批准号：
10680493
负责人：
KOJIMA Fumio
金额：
$ 1.92万
依托单位：
KOBE UNIVERSITY (1999-2000)Osaka Institute of Technology (1998)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

Our mission is to develop feasible computational methods for quantitative nondestructive evaluation related to eddy current testing with the background knowledge of computational intelligence. During the period of the research, our efforts were directed to the developments of the following inverse solvers.(1) Shape recovering algorithm using the GP-based fuzzy inference systemA quantitative nondestructive evaluation in eddy current testing was proposed by using genetic programming (GP) and fuzzy inference system. GP is applied to extract and select effective features from a probe impedance trajectory. With the use of the extracted features, a fuzzy inference system could detect presence, position, and size of a defect of test sample.(2) Fast computational inverse solver using the multivariate splinesIn this method, the model output was directly reconstructed by parameter space with data sets of the forward solutions. Hence the sensitivities of the output least square problem could be e … More valuated very fast. As a result, the tremendous amounts of computational savings have been achieved using the proposed scheme.(3) Evolutionary computation and its application to QNDEA coevolutionary algorithm (CEA) was effectively used for a crack shape identification problem where the number of shapes is unknown. A CEA including two populations of candidate shapes and of candidate combination was proposed. Results were demonstrated that the proposed method makes it possible to obtain the number of cracks as well as to identify their shapes.(4) Shape identification using the SQUID based NDE systemA quantitative nondestructive evaluation of using superconducting quantum interference devices (SQUIDs) was developed for conducting materials with a depth-varying crack. A computational method based on the genetic algorithm was proposed for recovering internal defect profiles with SQUID data.Consequently, all computations were successfully tested for the JSAEM benchmark problems, including the masked tube samples with natural cracks. Less

我们的任务是利用计算智能的背景知识，为涡流检测相关的定量无损评估开发可行的计算方法。在研究期间，我们的工作是针对以下反求解器的发展。(1)基于遗传规划的模糊推理系统的形状恢复算法，提出了一种基于遗传规划和模糊推理系统的涡流检测无损定量评价方法。将GP应用于探头阻抗轨迹的有效特征提取和选择。利用提取的特征，模糊推理系统可以检测出测试样本缺陷的存在、位置和大小。(2)基于多元样条的快速计算反求解方法该方法利用正解的数据集直接通过参数空间重构模型输出。因此，输出最小二乘问题的灵敏度可以很快地计算出来。因此，使用所提出的方案可以节省大量的计算量。(3)进化计算及其在QNDEA协同进化算法（CEA）中的应用，有效地解决了形状数量未知的裂纹形状识别问题。提出了包含两个候选形状种群和候选组合种群的CEA。结果表明，所提出的方法可以获得裂纹的数量和识别裂纹的形状。(4)基于SQUID的无损检测系统的形状识别。提出了一种利用超导量子干涉器件（SQUID）对具有深度变化裂纹的导电材料进行定量无损评价的方法。提出了一种基于遗传算法的利用SQUID数据恢复内部缺陷轮廓的计算方法。因此，所有的计算都成功地测试了JSAEM基准问题，包括具有自然裂纹的掩膜管样品。少