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Reconstruction of mode shapes using Shannon's sampling theorem and itsapplication to the nondestructive damage localization algorithm,

机译:使用香农采样定理重构模态形状及其在非破坏性损伤定位算法中的应用,

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Abstract: Shannon's sampling theorem is used here for the first time to reconstruct the mode shapes, resulting from equidistantly spaced sampling points obtained in the field, associated with building structures. The feasibility of reconstructing mode shapes of a structural system using a minimum number of sensors is investigated. To evaluate the feasibility of the method, the first three mode shapes of a simply supported beam and a mode shape represented by a parabolic equation are reconstructed. We find that the error between the true mode shapes and the reconstructed mode shapes reduces significantly as the number of the repeats of sampling points increases. The comparisons between exact and reconstructed mode shapes are presented, in addition to values for the modal assurance criteria (MAC) for the exact and reconstructed modes. The practicality of using these reconstructed mode shapes obtained via Shannon's sampling theorem is demonstrated using a recently developed nondestructive damage localization algorithm. The results show that the reconstructed mode shapes, generated using a minimum number of sensors, can indeed be used to localize damage. !7
机译:摘要:香农的采样定理在这里首次用于重建模态形状,该模态形状是由在现场获得的等距间隔的采样点与建筑结构相关联而得出的。研究了使用最少数量的传感器重建结构系统的模式形状的可行性。为了评估该方法的可行性,重建了简单支撑梁的前三个模态和由抛物线方程表示的模态。我们发现,随着采样点重复次数的增加,真实模式形状与重构模式形状之间的误差显着降低。除了精确和重构模式的模态保证标准(MAC)的值之外,还给出了精确和重构模式形状之间的比较。使用最近开发的非破坏性损伤定位算法证明了使用通过香农采样定理获得的这些重构模式形状的实用性。结果表明,使用最少数量的传感器生成的重构模式形状确实可以用于定位损坏。 !7

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