Quantitative evaluation of crack based on the sparse decomposition of array Lamb wave propagation

In-situ sparse-array imaging for through-thickness cracks in plates with A0 Lamb waves and an efficient waveform inversion algorithm

Structural health monitoring (SHM) requires efficient online crack detection and characterization to prevent structural failures, which mainly arise from fatigue cracks. Existing solutions for crack characterization involve analyzing sensed wave signals directly, but these approaches usually require onerous steps or many sensors to obtain sufficient and clear wave packets. An alternative strategy is a model-based inversion, which takes the full waveform into consideration and does not require analysis on a single wave packet. This approach can achieve accurate characterization with fewer sensors and simpler implementation. We propose an efficient model based on the Huygens' principle and the no-mode-conversion property of the A0 mode Lamb waves to meet the requirements of online monitoring. We then verify the proposed model-based crack imaging method through simulation and experiments on smooth and rough cracks. The proposed method is easy, cheap, and efficient, making it a desirable option for SHM tasks.