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首页> 外文期刊>IEEE Transactions on Magnetics >Arbitrary Crack Depth Profiling Through ACFM Data Using Type-2 Fuzzy Logic and PSO Algorithm
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Arbitrary Crack Depth Profiling Through ACFM Data Using Type-2 Fuzzy Logic and PSO Algorithm

机译：使用2型模糊逻辑和PSO算法通过ACFM数据进行任意裂纹深度剖析

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Estimating the shape and depth of cracks presented in metallic structures is one of the main issues of non-destructive testing (NDT) in order to evaluate effectively the structural integrity of a component. The alternating current field measurement (ACFM) technique is one of the most frequently used electromagnetic methods in this regard. Given the experimental nature of NDT methods, fuzzy logic-based methodologies have been widely used for solving the inverse problem. Due to some experimental restrictions, the obtained ACFM signals do not have high certainty. This problem usually leads to a high uncertainty of classical fuzzy rules extracted from low-accuracy ACFM signals. Therefore, applying classical fuzzy membership functions (MFs) exactly with maximum and fixed certainty does not lead to the best crack depth estimation. In this paper, a type-2 fuzzy logic system has been proposed to model the existing uncertainties of ACFM signals with a higher accuracy. Moreover, for regulating the uncertainty parameters of type-2 fuzzy MFs in the proposed model, the particle swarm optimization (PSO) algorithm has been used. Combining PSO with some special features existed in the ACFM signals allows the proposed model to be able to control the certainty of the extracted rules for estimating the exact depth of cracks. Then, the results of the proposed method are compared with the other state-of-the-art techniques for different levels of noise and different crack shapes obtained through simulated and empirical ACFM data. The results show the superiority of the proposed method even in conditions where the training database volume is not adequate.
机译：评估金属结构中出现的裂纹的形状和深度是无损检测（NDT）的主要问题之一，目的是有效评估部件的结构完整性。在这方面，交流场测量（ACFM）技术是最常用的电磁方法之一。鉴于NDT方法的实验性质，基于模糊逻辑的方法已广泛用于解决反问题。由于一些实验限制，获得的ACFM信号不确定性很高。该问题通常导致从低精度ACFM信号中提取的经典模糊规则的不确定性很高。因此，精确地以最大的固定确定性应用经典模糊隶属度函数（MF）不会导致最佳的裂纹深度估计。本文提出了一种2型模糊逻辑系统，以较高的精度对ACFM信号的现有不确定性进行建模。此外，为调节模型中的2型模糊MF的不确定性参数，使用了粒子群优化（PSO）算法。将PSO与ACFM信号中存在的某些特殊功能结合在一起，可以使所提出的模型能够控制提取规则的确定性，以估计出确切的裂纹深度。然后，将该方法的结果与通过模拟和经验ACFM数据获得的不同噪声水平和不同裂纹形状的其他最新技术进行比较。结果表明，即使在训练数据库量不足的情况下，该方法也具有优越性。

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