Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

作     者：Shengkang Zong Sheng Wang Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni Shengkang Zong;Sheng Wang;Zhitao Luo;Xinkai Wu;Hui Zhang;Zhonghua Ni

作者机构：Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical InstrumentsSchool of Mechanical EngineeringSoutheast UniversityNanjing 211189China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2023年第36卷第3期

页      面：252-261页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 1010[医学-医学技术（可授医学、理学学位）] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0702[理学-物理学] 1009[医学-特种医学] 

基　　金：Supported by National Natural Science Foundation of China(Grant Nos.52272433 and 11874110) Jiangsu Provincial Key R&D Program(Grant No.BE2021084) Technical Support Special Project of State Administration for Market Regulation(Grant No.2022YJ11). 

主　　题：Ultrasonic guided waves Singular value decomposition Damage detection and localization Environmental and operational conditions One-dimensional convolutional neural network 

摘      要：Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.