In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is $5\times 10^{-6}$ in the case of no noise addition; the optimal training MSE is $1.3\times 10^{-5}$ in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

在结构健康监测领域，基于振动的损伤识别仍然是一个艰巨的挑战。这一挑战的关键是在观察到的振动特性和结构损伤的实际状态（例如刚度降低）之间建立可靠的关联。这种关联不仅准确地表明了损坏的存在，而且还表明了损坏的位置和严重程度。为了解决这个复杂的模式识别问题，近年来出现了包括深度学习在内的大量方法。在本文中，我们提出了一种新的结构损伤识别方法，该方法利用结构的振动信息和基于Alex NET改进的卷积神经网络。该方法包括分别计算受损和未受损结构在冲击载荷作用下的加速度响应功率谱密度，然后将两个功率谱数据进行差分，随后将这些功率谱差值数据引入卷积神经网络进行训练。使用功率谱密度分析作为预处理步骤将时域信号转换为频域信号，这种转换允许卷积神经网络捕获和学习数据的特定频率特征，从而促进学习过程神经网络模型。本文通过数值模拟和实验验证对该方法的有效性进行了严格的评估，并在数值研究中添加了3%和5%的噪声来测试该方法的鲁棒性。卷积神经网络训练过程中，最优训练均方误差（MSE）为$5\times 10^{-6}$在没有添加噪声的情况下；最优训练 MSE 为$1。3\times 10^{-5}$在添加噪声的情况下。仿真和实验结果均证实了该方法在定位结构损伤方面具有较高的准确性和良好的鲁棒性。