文献类型：期刊文献

英文题名：Deep learning enables nonlinear Lamb waves for precise location of fatigue crack

作者：Xu, Haiming[1];Liu, Lishuai[1,3];Xu, Jichao[1];Xiang, Yanxun[1,2];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Mailbox 531,130 Meilong Rd, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Room 206,Bldg 14, Shanghai, Peoples R China

年份：2024

卷号：23

期号：1

起止页码：77

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

收录：;EI(收录号：20231714009149);WOS:【SCI-EXPANDED(收录号:WOS:000973349700001)】；

基金：The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Key Research and Development Plan of China (Grant No. 2021YFC3001802) and the National Natural Science Foundation of China (Grant Nos. U1930202, 12025403, and 12104155).

语种：英文

外文关键词：Fatigue crack localization; nonlinear Lamb waves; relative value label; Attention layer; convolutional neural network

摘要：Localization of fatigue cracks imposes immense significance to ensure the health of the engineering structures and prevent further catastrophic accidents. The nonlinear ultrasonic waves, especially the nonlinear Lamb waves, have been increasingly studied and employed for identifying micro-damages that are usually invisible to traditional linear ultrasonic waves. However, it remains a challenge to locate the fatigue cracks using nonlinear Lamb waves owing to the enormous difficulties in decoding location information from acoustic nonlinearity. Motivated by this, this work presents a data-driven method for precise location of fatigue crack using nonlinear Lamb waves. A 1D-Attention-convolutional neural network is developed to correlate the fatigue crack location with the wavelet coefficients at the second harmonic frequency of Lamb wave signals. The introduction of the Attention layer enables the models to pay more attention to the desired nonlinear features which dominates locating the fatigue crack. In particular, a convenient dataset creation scheme guided by the relative value label is proposed to generate sufficient data commonly required for deep learning approach. In addition, a lightweight single-excite-multiple-receive signal acquisition method is adopted instead of full-matrix capture method used in the traditional research, which highly improves detection efficiency. Numerical simulation and experimental validation manifest that the trained network can be used to establish the complex mapping between the nonlinear ultrasonic signals and the fatigue crack location features, so as to locate barely visible fatigue cracks. Our work provides a promising and practical way to facilitate nonlinear Lamb waves to accurately locate fatigue cracks in large-scale plate-like structures.