Enhancing Defect Detection Principal Component Analysis in Quantitative Non-Stationary Thermal Wave Imaging

Abstract

Active thermography has been emerging as a reliable non-destructive testing method due to its whole field, non-contact and non-invasive evaluation capability. It uses the acquired history of surface infrared emission over the object and subsequently obtains subsurface details. But faint contrast detail accompanied with spurious data generated in experimentation leads to false interpretations with raw data analysis. A suitable processing, which enhances the signal to noise ratio and un-correlate noise by separating the noise from other features facilitates enhanced detail extraction. Either pulse compression or feature separation methods accomplish this task and improve the potential defect detection performance.