Defect detection in CFRP structures using pulsed thermographic data enhanced by penalized least squares methods

Pulsed thermography (PT) is a widely used non-destructive testing (NDT) method for detecting defective regions in carbon fiber reinforced polymers (CFRP) structures. In order to improve the spatial and temporal resolution of thermographic data, thermographic signal reconstruction (TSR) is often adopted for data processing and analysis. However, TSR only performs data filtering along the time direction, while the spatial information is not exploited for noise reduction. In addition, TSR cannot handle the non-uniform backgrounds commonly existing in thermal images. To get around these problems, this paper extends the utilization of the penalized least squares methods to defect detection in CFRP structures. The experiment results show that, with the aid of penalized least squares, the defective regions in thermal images are characterized more clearly, while the signal-to-noise ratio (SNR) values are increased significantly.