Improving defect visibility in square pulse thermography of metallic components using correlation analysis

Infrared (IR) thermography has gained wide applications as an important non-destructive testing (NDT) technique. Improving defect visibility is critical to achieving an accurate detection result through IR thermography. In this study, we propose a novel approach to improving defect visibility in square pulse thermography (SPT) of metallic components. In the proposed approach, the correlation function of contrast (CFC) is defined for the first time. Based on the theories of heat conduction and of correlation analysis, the differences of CFC between defects and sound regions are determined. We found that the peak lag time of the CFC is an effective feature for discriminating defects and sound regions in SPT. A new image is then constructed using the peak lag time of the CFC to improve defect visibility. To verify the efficiency of the proposed approach, an experiment was conducted on a steel specimen, and the principle component analysis (PCA) and the presented approach were compared. The results show that through the proposed approach, defects in metallic components can be indicated more clearly and detected more accurately.