Sparse Structural Principal Component Thermography for Defect Signal Enhancement in Subsurface Defects Detection of Composite Materials

Statistical methods, such as Principal component thermography (PCT) and Sparse Principal component thermography (SPCT) have been widely used for signal enhancement of subsurface defects in pulsed thermographic (PT) detection of composite materials. However, PCT and SPCT mainly focus on the temporal variation of thermographic data while leaving the structural variation un-modeled. In this paper, a method of sparse structural principal component thermography ((SPCT)-P-2) is proposed. In (SPCT)-P-2, the operation of shift-sampling is first conducted to augment the original thermographic matrix and capture the structural relationships inside the original thermal images. After that, the sparse trick is applied to extract features for defects and reduce signals of noise and non-uniform background. In the case study, two carbon fiber reinforced polymer (CFRP) specimens are detected with PT and the proposed (SPCT)-P-2 is evaluated for visualization enhancing purpose. The results of the experiments have revealed the proposed method helps to highlight the defect signals during the augmentation process, thus showing higher flexibility in reducing interference from background signals. As a conclusion, compared to the original statistical methods, (SPCT)-P-2 has better performance in visualization enhancing of defects.