利用对称性约束神经网络，实现铝板损伤高精度检测与定位。
Symmetry constrained neural networks for detection and localization of damage in metal plates
- 基于传感器对称布局设计具对称性约束的神经网络。
- 损伤检测准确率超99%，定位误差仅2.58±0.12毫米。
- 适合结构健康监测中需高精度定位的金属板场景。
本文研究深度学习在薄铝板损伤检测与定位中的应用。实验在桌面装置上进行，于板面布置四个压电传感器，轮流激发兰姆波并由其余三个接收。训练神经网络分析材料响应的时间序列数据，当导波与接触载荷相互作用时，数据呈现损伤特征。最终模型实现超过99%的检测准确率，并达到2.58±0.12毫米的平均定位误差。针对每项任务，最优模型均基于传感器相似且呈正方形排列、板材近似均匀的先验知识设计，体现对称性诱导偏差的优势。
原文摘要 · Abstract (English)
The present paper is concerned with deep learning techniques applied to detection and localization of damage in a thin aluminum plate. We used data collected on a tabletop apparatus by mounting to the plate four piezoelectric transducers, each of which took turn to generate a Lamb wave that then traversed the region of interest before being received by the remaining three sensors. On training a neural network to analyze time-series data of the material response, which displayed damage-reflective features whenever the plate guided waves interacted with a contact load, we achieved a model that detected with greater than $99\%$ accuracy in addition to a model that localized with $2.58 \pm 0.12$ mm mean distance error. For each task, the best-performing model was designed according to the inductive bias that our transducers were both similar and arranged in a square pattern on a nearly uniform plate.
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