Migot Asaad, Saaudi Ahmed, Giurgiutiu Victor
Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.
Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA.
Sensors (Basel). 2025 Mar 20;25(6):1926. doi: 10.3390/s25061926.
This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments' signals were cropped and resized using MATLAB code to be used as input image datasets to train, validate, and test the proposed CNN model. Two scenarios were adopted from PAWS transducers. First, two sensors were positioned in two corners of the plate, while, in the second scenario, four sensors were used to monitor and collect the signals. Eight datasets were collected and reshaped from these two scenarios. These datasets presented the signals of two, three, four, and five impacts. The model's performance was evaluated using four metrics: confusion matrix, accuracy, precision, and F1 score. The proposed model demonstrated exceptional performance by accurately localizing all of the impact points of the first scenario and 99% of the second scenario. The main limitation of the proposed model is how to differentiate the data samples that have similar features. From our point of view, the similarity challenge arose from two factors: the segmentation interval and the impact distance. First, applying the segmenting procedure to the PWAS signals led to an increase in the number of data samples. The procedure segmented each PWAS signal to 30 samples with equal intervals, regardless of the features of the signal. Segmenting and transforming different PWAS signals into image-based data points led to data samples that had similar features. Second, some of the impacts had a close distance to the PWAS sensors, which resulted in similar segmented signals. Therefore, the second scenario was more challenging for the proposed model.
本文提出了一种新方法，利用所提出的二维卷积神经网络（CNN）和接收到的冲击信号来定位板状结构上的冲击事件。在测试板上安装了由四个压电晶片有源传感器（PWAS）组成的网络，以采集冲击信号。这些信号由反射波组成，反射波提供了有关冲击事件的有价值信息。在该方法中，每个接收到的信号被分成几个相等的段。然后，使用基于小波变换（WT）的时频分析来处理每个段信号。利用MATLAB代码对这些段信号生成的WT图进行裁剪和调整大小，用作输入图像数据集来训练、验证和测试所提出的CNN模型。从PAWS换能器采用了两种场景。第一种情况是，两个传感器位于板的两个角上，而在第二种场景中，使用四个传感器来监测和收集信号。从这两种场景中收集并重新整理了八个数据集。这些数据集呈现了两次、三次、四次和五次冲击的信号。使用四个指标评估模型的性能：混淆矩阵、准确率、精确率和F1分数。所提出的模型通过准确地定位第一种场景的所有冲击点和第二种场景的99%的冲击点，表现出了卓越的性能。所提出模型的主要局限性在于如何区分具有相似特征的数据样本。在我们看来，相似性挑战来自两个因素：分割间隔和冲击距离。首先，对PWAS信号应用分割程序导致数据样本数量增加。该程序将每个PWAS信号等间隔分割为30个样本，而不考虑信号的特征。将不同的PWAS信号分割并转换为基于图像的数据点会导致具有相似特征的数据样本。其次，一些冲击与PWAS传感器的距离很近，这导致了相似的分割信号。因此，第二种场景对所提出的模型更具挑战性。
