The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining convolutional neural networks (CNNs) and wavelet transform to analyze the laser-generated ultrasonic signals for detecting the width of subsurface defects accurately. The novelty of this work is to convert the laser ultrasonic signals into the scalograms (images) via wavelet transform, which are subsequently utilized as the image input for the pretrained CNN to extract the defect features automatically to quantify the width of defects, avoiding the necessity and inaccuracy induced by artificial feature selection. The experimentally validated numerical model that simulates the interaction of laser-generated ultrasonic waves with subsurface defects is first established, which is further utilized to generate adequate laser ultrasonic signals for training the CNN model. A total number of 3104 data are obtained from simulation and experiments, with 2480 simulated signals for training the CNN model and the remaining 620 simulated data together with 4 experimental signals for verifying the performance of the proposed algorithm. This approach achieves the prediction accuracy of 98.5% on validation set, particularly with the prediction accuracy of 100% for the four experimental data. This work proves the feasibility and reliability of the proposed method for quantifying the width of subsurface defects and can be further expanded as a universal approach to various other defects detection, such as defect locations and shapes.

中文翻译：

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通过使用卷积神经网络和小波变换分析激光超声信号自动量化次表面缺陷。

传统的机器学习算法用于分析超声波信号以检测结构缺陷，必须手动识别和提取时域或频域特征，其可靠性和有效性存在问题。这项工作提出了一种通过结合卷积神经网络 (CNN) 和小波变换来分析激光产生的超声信号以准确检测地下缺陷宽度的新方法。这项工作的新颖之处在于通过小波变换将激光超声信号转换为尺度图（图像），随后将其作为图像输入用于预训练的 CNN 自动提取缺陷特征以量化缺陷的宽度，避免了必要性以及人工特征选择引起的不准确性。首先建立了模拟激光产生的超声波与次表面缺陷相互作用的经过实验验证的数值模型，进一步利用该模型产生足够的激光超声信号来训练 CNN 模型。仿真和实验共得到3104个数据，其中2480个仿真信号用于训练CNN模型，其余620个仿真数据与4个实验信号一起用于验证所提出算法的性能。该方法在验证集上实现了 98.5% 的预测准确率，特别是对四个实验数据的预测准确率达到了 100%。