Damage localization in plate-like structures using time-varying feature and one-dimensional convolutional neural network
Damage localization in plate-like structures using time-varying feature and one-dimensional convolutional neural network
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DOI：
10.1016/j.ymssp.2020.107107
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发表时间：
2021-01
影响因子：
8.4
通讯作者：
Shengyuan Zhang;C. Li;W. Ye
中科院分区：
文献类型：
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作者：
Shengyuan Zhang;C. Li;W. Ye
用于板状结构中的损伤检测和定位的基于兰姆波的SHM技术通常依赖于超声导波的后处理。传统的损伤定位方法是利用损伤散射波的飞行时间（TOF）来实现的。然而，这种方法通常需要从波信号中识别纯模式，这在许多情况下是困难的。基于损伤指数（DI）的方法提供了另一种类型的方法，不需要这样的信号解释。由于单独的DI不包含时间信息，因此必须执行来自多个致动器-传感器对的信号的数据融合以用于定位。因此，需要相对密集的致动器-传感器网络，并且定位只能在网络覆盖的区域内实现。意识到包含在波信号中的时间信息是非常重要的损伤定位，我们提出了一个随时间变化的DI功能，保留的时间信息，以提高定位精度。此外，我们建议使用一维卷积神经网络（1-D CNN）直接与损伤位置的时变DI相关。CNN的等方差属性保留了时间信息。CNN的效率和特征提取能力有助于建立具有一定泛化能力的神经网络模型，从而在一个板块上训练的模型可以适用于新的板块。所提出的方法的性能被证明在三种情况下：定位在同一个板与不同的损伤位置，定位在一个新的板与相同的损伤位置，定位在一个新的板，但与不同的损伤位置。尽管只使用了四个传感器，有限的实验数据的培训，已取得了良好的效果。性能与其他几个现有的方法进行了比较。
Lamb wave-based SHM technology for damage detection and localization in plate-like structures has typically relied on post-processing of ultrasonic guided waves. Traditionally, the damage localization is realized using the time-of-flight (TOF) of damage-scattered waves. However, this method often requires the identification of a pure mode from the wave signal which is difficult in many cases. Damage index (DI) based methods offer another type of approaches that do not need such singal explanation. Since DI alone doesn’t contain temporal information, data fusion of signals from multiple actuator-sensor pairs must be performed for localization. As a result, a relatively dense actuator-sensor network is needed, and localization can only be realized within the region covered by the network. Realizing that temporal information contained in the wave signal is extremely important to damage localization, we propose a time-varying DI feature that preserves the temporal information to improve localization accuracy. In addition, we propose to use one-dimensional convolutional neural network (1-D CNN) to correlate the time-varying DI directly with the damage location. The equivariance property of CNN preserves the temporal information. The efficiency and feature extraction capability of the CNN help to build a neural network model with certain generalization capability, and thus the model trained on one plate can be applicable to a new plate. The performance of the proposed method was demonstrated in three cases: localization in the same plate with different damage locations, localization in a new plate with the same damage locations, and localization in a new plate but with different damage locations. Despite that only four transducers were used, and limited experimental data for training were available, good results have been obtained. Performance comparison with several other existing methods was also conducted.