Non-Destructive Evaluation of Hidden Defects Beneath the Multilayer Organic Protective Coatings Based on Terahertz Technology

An organic protective coating system plays an important role for the corrosion protection of offshore metallic structures. It is of practical importance to detect possible coating defects and evaluate coating performance for corrosion degradation monitoring. Reliable defect identification can provide timely and effective maintenance to avoid serious consequences. This work investigated various multilayer organic coating systems by terahertz pulse imaging technology and aimed to explore the inspection ability of terahertz non-destructive testing (THz NDT) of protective coatings. Several types of defective coating samples were measured. The comparison of measurements obtained for sites with and without defects was provided. The changes in the signals caused by the presence of defects were explained. Structural analysis, quantitative evaluation, and defect identification were carried out in detail. The results of measurements showed that corrosion defects, paint bulge defects, and paint detachment defects can be distinguished and identified in combination with the causes of defects. It can provide effective technical guidance for terahertz technology to be gradually extended to engineering applications.

Terahertz pulse imaging: A novel denoising method by combing the ant colony algorithm with the compressive sensing

Terahertz (THz) pulse imaging exhibits a potential application in biomedicine, nondestructive detection and safety inspection. However, the THz time-domain spectroscopy system will affect the THz image quality. To improve the THz image quality, in this article we proposed a novel method by combining the ant colony algorithm with the compressive sensing method. First, the image edge is detected by using the ant colony algorithm. Subsequently, the compressive sensing method based on signal sparse representation and the reconstruction algorithm from partial Fourier is applied on the non-edge image for noise reduction. Finally, the reconstruction result is obtained by combining the noise reduced non-edge image with the edge image. The experimental results on three kinds of images prove that the proposed method can preserve the edge information during noise reduction.