基于自适应聚类与语义加权的空气耦合兰姆波无基准损伤成像方法

doi:10.19936/j.cnki.2096-8000.20260428.006

摘要/Abstract

摘要： 针对传统空气耦合兰姆(Lamb)波概率成像方法所存在的严重依赖无缺陷参考信号及易受边界伪影干扰的问题,提出一种融合密度聚类与语义加权的无基准概率成像新方法。该方法通过正交扫查采集全路径响应信号,提取小波低频近似系数与归一化对称差分因子构建联合特征向量,并采用基于密度的噪声应用空间聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)对扫描路径进行“健康-损伤”状态的无监督划分。进一步将聚类获得的语义标签作为自适应权重嵌入改进的损伤概率检测重构算法(Reconstruction Algorithm for Probabilistic Inspection of Damage,RAPID)概率成像模型,实现伪影路径抑制与软基准的动态构建。在碳纤维增强复合材料分层缺陷检测试验中,所提方法在完全无需独立健康基准的条件下,平均尺寸测量误差较传统RAPID方法降低50.5%,对40 mm×20 mm×0.05 mm缺陷在X/Y方向的测量误差分别降低81.7%与65.5%,并实现了对20 mm×20 mm×0.05 mm小缺陷的有效识别。在低至5 dB信噪比的强噪声环境中,该方法仍保持稳定的成像性能。研究表明,该方法有效解决了传统成像方法对健康基准数据的依赖问题,显著提升了损伤检测的精度与鲁棒性,展现出良好的工程应用潜力。

关键词: 无基准成像, 密度聚类, 语义加权, 空气耦合兰姆波, 碳纤维增强复合材料, 边界伪影抑制

Abstract: This paper proposes a baseline-free probability imaging method integrating density clustering and semantic weighting to overcome the reliance on defect-free reference signals and boundary artifacts in traditional air-coupled Lamb wave imaging. Full-path response signals are collected via orthogonal scanning. A joint feature vector is constructed using wavelet low-frequency approximation coefficients and a normalized symmetric difference factor, followed by density-based spatial clustering of applications with noise (DBSCAN) to unsupervisedly classify scan paths into “healthy” or “damaged” states. The resulting semantic labels are embedded as adaptive weights into an improvedreconstruction algorithm for probabilistic inspection of damage (RAPID) model, suppressing artifact paths and dynamically constructing a soft baseline. Experiments on CFRP delamination defects show that the method, requiring no prior baseline, reduces the average size measurement error by 50.5% compared to traditional RAPID. For a 40 mm×20 mm×0.05 mm defect, measurement accuracy in the X/Y directions has improved by 81.7% and 65.5%, respectively, while effective identification of a 20 mm×20 mm×0.05 mm defect is achieved. Stable imaging performance is maintained even at a 5 dB SNR. This method effectively eliminates the baseline dependency and enhances detection accuracy and robustness, demonstrating significant engineering potential.

Key words: baseline-free imaging, density clustering, semantic weighting, air-coupled Lamb waves, CFRP, boundary artifact suppression

中图分类号:

TB332

秦塬, 周进节, 彭志康. 基于自适应聚类与语义加权的空气耦合兰姆波无基准损伤成像方法[J]. 复合材料科学与工程, 2026, 0(4): 44-54.

QIN Yuan , ZHOU Jinjie , PENG Zhikang. A baseline-free damage imaging method for air-coupled lamb waves based on adaptive clustering and semantic weighting[J]. COMPOSITES SCIENCE AND ENGINEERING, 2026, 0(4): 44-54.

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