Department of Civil Engineering, National Taiwan University, Taipei City 10617, Taiwan.
Department of Civil Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan.
Sensors (Basel). 2021 May 18;21(10):3514. doi: 10.3390/s21103514.
In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens' embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases.
为了准确诊断高阶静不定结构的健康状况，大多数现有的结构健康监测 (SHM) 方法都需要多个传感器来收集足够的信息。然而，在实践中，对于高自由度结构来说，从多个传感器进行全面的数据收集通常是不可用的。我们提出了一种方法，可以协调这两个看似矛盾的困难。Takens 嵌入定理用于增加从单个传感器收集的数据的维度。利用机器学习在图像分类方面的成功，高维重构吸引子被转换为图像，并输入到卷积神经网络 (CNN) 中。对一个 3 层剪切框架结构的 10 种损伤情况进行了吸引子分类。数值结果表明，CNN 模型固有的高维性允许处理更高维的数据。成功地嵌入了损伤程度和位置的信息。相同的方法将允许提取具有无监督 CNN 分类的数据，使其与实际用例一致。
