Structural damage detection is of very importance to improve reliability and safety of civil structures. A novel sensor data-driven structural damage detection method is proposed in this paper by combining continuous wavelet transform (CWT) with deep convolutional neural network (DCNN). In this method, time-frequency images are obtained by CWT from original one-dimensional sensor signals. And, DCNN is designed to mine structural damage features from the time-frequency images and distinguish different structural damage condition. The proposed method is carried out on three-story building structure dataset and steel frame dataset. The experimental results show that the proposed method has the high accuracy and robustness of the damage detection compared with other existing machine learning methods.

结构损伤检测对于提高民用建筑的可靠性和安全性非常重要。结合连续小波变换（CWT）和深度卷积神经网络（DCNN），提出了一种新的传感器数据驱动的结构损伤检测方法。在这种方法中，通过CWT从原始的一维传感器信号中获取时频图像。并且，DCNN旨在从时频图像中挖掘结构破坏特征并区分不同的结构破坏条件。该方法在三层建筑结构数据集和钢框架数据集上进行。实验结果表明，与现有的其他机器学习方法相比，该方法具有较高的检测精度和鲁棒性。