文献类型：期刊文献

英文题名：A Hybrid Deep Learning Framework Based on Diffusion Model and Deep Residual Neural Network for Defect Detection in Composite Plates

作者：Huang, Tianrui[1];Gao, Yang[1];Li, Zhenglin[1];Hu, Yue[1];Xuan, Fuzhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China

年份：2023

卷号：13

期号：10

外文期刊名：APPLIED SCIENCES-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000995606900001)】；

基金：This project was supported by the National Natural Science Foundation of China (Grant Nos. 52275146, 51835003, 61804054 and 12174102), supported by State Key Laboratory of New Textile Materials and Advanced Processing Technologies No. XXXFZ2022006, and supported by "the Fundamental Research Funds for the Central Universities".

语种：英文

外文关键词：structural health monitoring; diffusion model; DenseNet; Lamb wave

摘要：The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies, such as deep learning, have become one of the main driving forces for state monitoring and predictive analysis of these structures. However, it is difficult to obtain sufficient data to train the deep learning model, which may fail to build an accurate and efficient SHM model. To overcome this problem, a new method based on Lamb waves and the diffusion model (DM) is proposed to realize the identification and classification of different defects for carbon-fiber-reinforced polymer (CFRP) structures. In this study, DM is used as the generation model of data enhancement, and the optimized and improved DDPM model is constructed in this experiment. The deep residual neural network (DenseNet) is used to identify and classify the defect features from the Lamb wave signals. Experimental and test results show that the deep learning framework designed in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify damage signals of common defects in CFRP composite plates.