文献类型：期刊文献

英文题名：Physical-guided graph deep learning for composite pipelines structural health monitoring

作者：Jiang, Xiaoqian[1,2];Hu, Yue[1,2];Cao, Shuai[3];Cui, Fangsen[4];Li, Fucai[5];Gao, Yang[1,2,6];Xuan, Fu-zhen[1,2]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Shanghai Key Lab intelligent Sensing & Detect Tech, Shanghai, Peoples R China;[3]WS Audiol, Singapore, Singapore;[4]ASTAR, Inst High Performance Comp, Singapore, Singapore;[5]Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China;[6]Wuhan Text Univ, State Key Lab New Text Mat & Adv Proc Technol, Wuhan, Peoples R China

年份：2025

外文期刊名：STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL

收录：;EI(收录号：20255119735417);WOS:【SCI-EXPANDED(收录号:WOS:001569472400001)】；

基金：The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the National Natural Science Foundation of China (grants nos. 52105113, 52321002, 52175104, and U2468210), the Fundamental Research Funds for the Central Universities, State Key Laboratory of New Textile Materials and Advanced Processing Technologies (grant no. FZ2022006), and the Science and Technology Plan Project of State Administration for Market Regulation (grant no. 2023MK133).

语种：英文

外文关键词：graph deep learning; structural health monitoring; composite pipelines; physical rules; graph construction

摘要：Composite pipelines, widely applied in the oil and gas industry due to their high strength-to-weight ratio and corrosion resistance, are nonetheless vulnerable to damage under harsh operating conditions, making structural health monitoring (SHM) crucial for enhancing safety and preventing potential failures. However, the complex service environment and anisotropic properties of composite pipelines pose challenges for traditional SHM methods, which often struggle to extract underlying features and identify structural defects. To address these challenges, a new graph deep learning method, termed physical-guided graph deep learning (PGGDL), which leverages physical rules to enhance defect detection accuracy in composite pipeline health monitoring, is proposed in this study. The PGGDL method constructs graph data by integrating signals based on physical rules, including the sensor arrangement and the guided wave propagation mechanism, enabling efficient data fusion. Meanwhile, the PGGDL constructs a temporal model and a graph attention network to capture both node-level and global spatial features. Furthermore, an adaptive learning rate strategy is designed to dynamically adjust the learning rate, improving training efficiency, achieving faster convergence, and enhancing model stability. Four experimental cases are conducted to demonstrate that the PGGDL outperforms traditional deep learning models in accurately detecting and localizing pipeline defects with limited data.