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

我们的使命是开发可行的计算方法，用于与涡流测试有关的定量无损评估，并具有计算智能的背景知识。在研究期间，我们的努力是针对以下反求解器的发展。（1）使用基于GP的模糊推理系统A恢复算法的形状恢复算法，在涡流测试中使用基因程序（GP）（GP）和模糊的推论系统提出了涡流测试中的定量非破坏性评估。 GP用于从探针阻抗轨迹中提取并选择有效特征。通过使用提取的特征，模糊的推理系统可以检测到测试样品的缺陷的存在，位置和大小。（2）使用多元子花线使用该方法，快速计算逆求器，该方法通过参数空间直接通过远期解决方案的数据集来直接重构。因此，输出最小成方问题的敏感性可能是E…更加值得更快。结果，使用拟议方案实现了大量的计算节省。（3）进化计算及其在QNDEA协同进化算法（CEA）中的应用有效地用于裂纹形状识别问题，而形状数的数量未知。提出了包括两个候选形状和候选组合的CEA。结果证明了所提出的方法使得可以获得裂纹的数量以及识别其形状。（4）使用基于鱿鱼的NDE SystemA定量无损评估使用超导量子干扰设备（Squid）的形状识别，用于使用超深层裂纹进行导电材料。提出了一种基于遗传算法的计算方法，用于恢复使用鱿鱼数据恢复内部缺陷曲线。结果，所有计算均已成功测试了JSAEM基准问题，包括带有自然裂缝的蒙面管样品。较少的