文献类型：期刊文献

英文题名：Damage Detection for Pressure Vessels Using a New Graph Neural Network Framework

作者：Jiang, Xiaoqian[1]; Hu, Chaojie[2]; Hu, Yue[1]; Gao, Yang[1]; Su, Wensheng[3]; Xue, Zhigang[3]

机构：[1] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China; [2] School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China; [3] Institute of Jiangsu Province, Spencial Equipment Supervision Inspection, Najing, China

年份：2024

起止页码：1236

外文期刊名：2024 10th Asia Conference on Mechanical Engineering and Aerospace Engineering, MEAE 2024

收录：EI(收录号：20253118907711)

语种：英文

外文关键词：Corrosion - Damage detection - Graph neural networks - Ultrasonic applications

摘要：Pressure vessels, under harsh operating conditions, are prone to damage from external forces, media interactions, and thermal effects, potentially leading to dangerous incidents like leaks, corrosion, and cracks. To detect damage without compromising equipment performance, the study first converts complex ultrasonic guided wave data into graph data, employing a sparse sampling method that utilizes data from only one excitation sensor to construct the graph. Subsequently, feature extraction module based on graph neural network combined with graph pooling techniques is designed to extract features and achieve precise damage localization and detection. Experimental results indicate that this method demonstrates high accuracy and robustness in the task of pressure vessel damage localization, offering a promising technique for intelligent monitoring of industrial equipment. ? 2024 IEEE.