文献类型：期刊文献

英文题名：Deep learning-based solvability of underdetermined inverse problems in nonlinear ultrasonic characterization of micro damages

作者：Liu, Lishuai[1];Sun, Di[1];Xiang, Yanxun[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety MOE, Shanghai 200237, Peoples R China

年份：2022

卷号：132

期号：14

外文期刊名：JOURNAL OF APPLIED PHYSICS

收录：;EI(收录号：20224212978747);WOS:【SCI-EXPANDED(收录号:WOS:000886038600015)】；

基金：ACKNOWLEDGMENTSThis work was supported by the National Natural Science Foundation of China (NNSFC) (Grant Nos. 12104155, 12025403, and U1930202), the Natural Science Foundation of Shanghai (Grant No. 21ZR1417100), and the Shanghai Sailing Program (Grant No. 21YF1408900).

语种：英文

外文关键词：Damage detection - Deep learning - Differential equations - Discrete wavelet transforms - Inverse problems - Surface waves - Ultrasonic testing - Ultrasonic waves

摘要：Detection and evaluation of micro-damages in the early stages of engineering failure are crucial for various industrial structures to ensure their safety and prevent further catastrophic accidents. The nonlinear ultrasonic technique (NUT) has gained increasing popularity and recognition for breaking through the detection sensitivity limit upon micro-damages that usually are invisible to conventional linear techniques. However, it remains an ongoing challenge to quantitatively characterize micro-damages using NUT due to great difficulties in fully modeling the complicated interaction mechanism between the nonlinear ultrasonic waves and micro-damages. This work presents a data-driven perspective for solving multiparameter underdetermined inverse problems that are at the core of NUT, while allowing by-passing the creation of high-fidelity physics-based models. Nonlinear Lamb wave measurements with group-velocity mismatching are conducted to introduce both size and localization information of damages to the assembled dataset. A nonlinearity-aware discrete wavelet transform-bidirectional long short-term memory network is proposed to directly process nonlinear ultrasonic responses to automatically model latent nonlinear dynamics, thus establishing the complex mapping between the nonlinear ultrasonic signals and the multi-dimensional damage features. In particular, an attempt is made to augment the physical explainability of the proposed deep learning approach through a frequency component importance analysis. The trained network enables accurate and explainable predictions of length and localization of closed cracks and robustness against varying degrees of noise. Our work paves a promising and practical way to promote the transformation of NUT from the qualitative analysis for accurate and efficient quantitative prediction.