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Defective Areas Identification in Aircraft Components by Bivariate EMD Analysis of Ultrasound Signals

机译:超声信号的二抗体EMD分析飞机组件的缺陷区域识别

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In recent years many alternative methodologies and techniques have been proposed to perform non-destructive inspection and maintenance operations of moving structures. In particular, ultrasonic techniques have shown to be very promising for automatic inspection systems. From the literature, it is evident that the neural paradigms are considered, by now, the best choice to automatically classify ultrasound data. At the same time the most appropriate pre-processing technique is still undecided. The aim of this paper is to propose a new and innovative data pre-processing technique that allows the analysis of the ultrasonic data by a complex extension of the Empirical Mode Decomposition (EMD). Experimental tests aiming to detect defective areas in aircraft components are reported and a comparison with classical approaches based on data normalization or wavelet decomposition is also provided.
机译:近年来,已经提出了许多替代方法和技术来执行移动结构的非破坏性检查和维护操作。特别地,超声波技术已显示为自动检测系统非常有前途。从文献中,明显看出,目前,通过自动分类超声数据的最佳选择是考虑神经范式的最佳选择。同时仍未确定最合适的预处理技术。本文的目的是提出一种新的和创新的数据预处理技术,允许通过经验模式分解(EMD)的复杂扩展来分析超声数据。还提供了旨在检测​​飞机组件中有缺陷区域的实验测试,并提供了基于数据归一化或小波分解的经典方法的比较。

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