Katunin Andrzej
Department of Fundamentals of Machinery Design, Faculty of Mechanical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland.
Sensors (Basel). 2021 Jan 21;21(3):714. doi: 10.3390/s21030714.
The paper aims to analyze the performance of the damage identification algorithms using the directional wavelet transforms, which reveal higher sensitivity for various orientations of spatial damage together with lower susceptibility to noise. In this study, the algorithms based on the dual-tree, the double-density, and the dual-tree double-density wavelet transforms were considered and compared to the algorithm based on the discrete wavelet transform. The performed analyses are based on shearographic experimental tests of a composite plate with artificially introduced damage at various orientations. It was shown that the directional wavelet transforms are characterized by better performance in damage identification problems than the basic discrete wavelet transform. Moreover, the proposed approach based on entropic weights applicable to the resulting sets of the detail coefficients after decomposition of mode shapes can be effectively used for automatic selection and emphasizing those sets of the detail coefficients, which contain relevant diagnostic information about damage. The proposed processing method allows raw experimental results from shearography to be significantly enhanced. The developed algorithms can be successfully implemented in a shearographic testing for enhancement of a sensitivity to damage during routine inspections in various industrial sectors.
本文旨在分析使用方向小波变换的损伤识别算法的性能，该变换对空间损伤的各种方向具有更高的灵敏度，同时对噪声的敏感度较低。在本研究中，考虑了基于双树、双密度和双树双密度小波变换的算法，并与基于离散小波变换的算法进行了比较。所进行的分析基于对一块在不同方向人工引入损伤的复合板的剪切散斑实验测试。结果表明，与基本离散小波变换相比，方向小波变换在损伤识别问题中具有更好的性能。此外，所提出的基于熵权的方法适用于模态形状分解后得到的细节系数集，可有效地用于自动选择和强调那些包含有关损伤的相关诊断信息的细节系数集。所提出的处理方法可以显著增强剪切散斑的原始实验结果。所开发的算法可以成功地应用于剪切散斑测试中，以提高在各工业部门常规检查期间对损伤的敏感度。
