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A probabilistic approach for quantitative identification of multiple delaminations in laminated composite beams using guided waves

机译:一种利用导波定量识别层压复合梁中多个分层的概率方法

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In this study a probabilistic approach is proposed to identify multiple delaminations in laminated composite beams using guided waves. The proposed method is a model-based approach, which provides a quantitative identification of the delaminations. This study puts forward a practical damage identification method, and hence, it can identify multiple delaminations using guided wave signal measured at a single measurement point on the laminated composite beams. The proposed method first determines the number of delaminations using Bayesian model class selection method. The Bayesian statistical framework is then employed to not only identify the delamination locations, lengths and through-thickness locations, but also quantify the associated uncertainties, which provides valuable information for engineers in making decision on necessary remedial work. In addition the proposed method employs the time-domain spectral finite element method and Bayesian updating with Subset simulation to further improve the computational efficiency. The proposed probabilistic approach is verified and demonstrated using data obtained from numerical simulations, which consider both measurement noise and modeling error, and experimental data. The results show that the proposed method can accurately determine the number of delaminations, and the identified delamination locations, lengths and through-thickness locations are closed to the true values. (C) 2016 Elsevier Ltd. All rights reserved.
机译:在这项研究中,提出了一种概率方法,以使用导波识别层压复合梁中的多个分层。所提出的方法是基于模型的方法,其提供了分层的定量识别。这项研究提出了一种实用的损伤识别方法,因此,它可以使用在复合梁上的单个测量点处测量的导波信号来识别多个分层。提出的方法首先使用贝叶斯模型类别选择方法确定分层数。然后,使用贝叶斯统计框架不仅可以识别分层位置,长度和贯穿厚度的位置,还可以量化相关的不确定性,这为工程师做出必要的补救工作决策提供了有价值的信息。此外,本文提出的方法采用时域谱有限元方法和贝叶斯更新与子集仿真,以进一步提高计算效率。所提出的概率方法已使用从数值模拟获得的数据进行了验证和演示,该数据同时考虑了测量噪声和建模误差以及实验数据。结果表明,所提出的方法可以准确地确定分层的数量,并且所识别的分层位置,长度和贯穿厚度的位置接近真实值。 (C)2016 Elsevier Ltd.保留所有权利。

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