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A fuzzy alignment approach to sizing surface cracks by the AC field measurement technique

机译：利用交流场测量技术模糊对准表面裂纹的方法

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摘要

In real world applications, one major issue with most of non-phenomenological methods is the need for a large and complete databanks. In most practical cases, it is impossible to obtain a complete and representative database that includes sufficient number of representative examples. This fact renders most of available defects and cracks databases useless. In this paper, an aligning method is formalized by a fuzzy recursive least square algorithm as a learning methodology for electromagnetic alternative current field measurement (ACFM) probe signals of a crack (data). This method along with a set of fuzzy linguistic rules, including adequate adaptation of different crack shapes (knowledge) for combining knowledge and data whenever the superposition theory can be utilized, provide a means to compensate for the lack of sufficient samples in available crack databases. We have shown that the combination of this fuzzy inference method and the method of the adaptation for different crack shapes provides sufficient means as a priori empirical knowledge for the training system. This approach significantly reduces the need for a large and complete crack databases. The validity of the proposed methodology is demonstrated by examining the sizing errors in the case of several surface cracks with elliptical depth profile when inverting their respective ACFM signals.
机译：在现实世界的应用程序中，大多数非现象学方法的一个主要问题是需要庞大而完整的数据库。在大多数实际情况下，不可能获得包含足够数量的代表性示例的完整且具有代表性的数据库。这个事实使大多数可用的缺陷和破解数据库变得无用。在本文中，通过模糊递归最小二乘算法将对准方法形式化，作为一种用于裂纹（数据）电磁交变电流场测量（ACFM）探测信号的学习方法。该方法以及一组模糊语言规则，包括只要可以利用叠加理论就可以适当地适应不同的裂纹形状（知识）以将知识和数据结合起来，从而提供了一种方法来补偿可用裂纹数据库中缺少足够的样本。我们已经表明，这种模糊推理方法与针对不同裂纹形状的适应方法的结合提供了足够的手段，作为训练系统的先验经验知识。这种方法大大减少了对大型完整的破解数据库的需求。通过在反转其各自的ACFM信号时，在具有椭圆深度轮廓的多个表面裂纹的情况下检查尺寸误差，证明了所提出方法的有效性。

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