Computationally efficient delamination detection in composite beams using Haar wavelets
Computationally efficient delamination detection in composite beams using Haar wavelets
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DOI：
10.1016/j.ymssp.2011.02.003
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发表时间：
2011-08
影响因子：
8.4
通讯作者：
H. Hein;L. Feklistova
中科院分区：
文献类型：
--
作者：
H. Hein;L. Feklistova
本文提出了一种基于振动的集成方法，用于均匀梁和复合梁的分层检测。该方法基于 Haar 小波和人工神经网络 (ANN)。首先，采用 Chen-Hsiao 方法（CHM）将结构的标度模态响应展开为 Haar 级数，并构建分层特征指标。不同的人工神经网络利用基于Haar小波和基于频率的方法建立的68个数据集的数据库来建立分层状态与分层特征指数或频率之间的映射关系。将结果相互比较。仿真结果表明，所提出的带有分层指数的复杂方法可以高精度（>90%）检测分层位置并识别分层程度；该方法比基于频率的方法需要更少的计算和处理时间。
The paper presents an integrated vibration-based method for delaminations detection in homogeneous and composite beams. The method is based on Haar wavelets and artificial neural networks (ANNs). Firstly, scaled modal responses of the structure are expanded into Haar series by Chen–Hsiao method (CHM), and a delamination feature index is constructed. The database of 68 datasets built on Haar wavelet and frequency-based approaches was utilized by different ANNs to establish the mapping relationship between the delamination status and the delamination feature index or frequencies. The results are compared to each other. The simulations show the proposed complex method with delamination index detects the location of delaminations and identifies the delamination extent with high precision (>90%); the approach requires less computations and processing time than the frequency-based approach.