计算机科学
卷积神经网络
稳健性(进化)
连续小波变换
人工智能
模式识别(心理学)
帧(网络)
深度学习
小波变换
可靠性(半导体)
小波
离散小波变换
电信
基因
量子力学
物理
生物化学
功率(物理)
化学
作者
Zuoyi Chen,Yanzhi Wang,Jun Wu,Chao Deng,Kui Hu
标识
DOI:10.1007/s10489-020-02092-6
摘要
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.
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