Tu Katherine, Ibarra-Castanedo Clemente, Sfarra Stefano, Yao Yuan, Maldague Xavier P V
Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.
Department of Electrical and Computer Engineering, Laval University, Québec City, QC G1V 0A6, Canada.
Sensors (Basel). 2021 Apr 16;21(8):2806. doi: 10.3390/s21082806.
Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.
红外热成像技术已在材料结构检测的诸多应用中得到广泛采用,在这些应用中,常采用数据分析方法来处理原始热数据并表征材料结构特性。在此,提出了一种多尺度热成像数据分析框架并将其应用于建筑结构检测。具体而言,首先通过进行太阳荷载热成像收集热成像图,然后通过多维总体经验模态分解将其在不同空间尺度下分解为若干固有模态函数。在每个尺度上,实施主成分分析(PCA)进行特征提取。通过可视化PCA的载荷向量,突出显示重要的建筑结构。与直接将PCA应用于原始热数据的主成分热成像相比,所提出的多尺度分析方法能够放大不同类型的结构特征。