文献类型：期刊文献

英文题名：Guided Wave Damage Location of Pressure Vessel Based on Optimized Explainable Convolutional Neural Network for Multivariate Time Series Classification Neural Network

作者：Zhang, Junxuan[1];Hu, Chaojie[1];Yan, Jianjun[1];Hu, Yue[1];Gao, Yang[1,2];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;[2]Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China

年份：2023

卷号：145

期号：4

外文期刊名：JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME

收录：;EI(收录号：20244317249707);WOS:【SCI-EXPANDED(收录号:WOS:001021550900006)】；

基金：& nbsp;National Natural Science Foundation of China (Grant Nos. 52275146, 51835003, 61804054, and 12174102; Funder ID: 10.13039/501100001809).The Open Project Program of Wuhan National Laboratory for Optoelectronics (No. 2020WNLOKF007; Funder ID: 10.13039/501100008416).

语种：英文

外文关键词：pressure vessel; guided wave; XCM; damage location

摘要：Guided wave is a key nondestructive technique for structural health monitoring due to its high sensitivity to structural changes and long propagation distance. However, to achieve high accuracy for damage location, large quantities of samples and thousands of iterations are typically needed for detection algorithms. To address this, in this paper, an eXplainable Convolutional neural network for Multivariate time series classification (XCM) is adopted, which is composed of one-dimensional (1D) and two-dimensional (2D) convolution layers to achieve high accuracy damage location on pressure vessels with limited training sets. By further optimizing the network parameters and network structure, the training time is greatly reduced and the accuracy is further improved. The optimized XCM improves the damage location precision from 95.5% to 98% with small samples (training set/validation set/testing set = 23/2/25) and low training epochs (under 100 epochs), suggesting that the XCM has great advantages in pressure vessel's damage location classification its potential for guided wave-based damage detection techniques in structural health monitoring.