Application of KPCA-based infrared thermal wave radar imaging in the detection of internal defects in carbon steel materials

Abstract

Detecting defects such as porosity, cracks and slag in oil pipeline welds Internal defects are essential to prevent potential safety risks. Infrared thermal wave radar has the advantages of high resolution, high efficiency and large detection depth, but its temperature response signal is often disturbed by environmental factors and non-uniform heating noise, which leads to the thermal response information of defects being covered by noise and affects the accuracy of detection. In order to solve this problem, this paper proposes a new method combining kernel principal component analysis and infrared thermal wave radar imaging technology (TWR). The KPCA algorithm is used to extract the nonlinear features in the thermal response data, and the dimension reduction processing and reconstruction are carried out. The time delay map with defect depth feature and the phase map with defect space feature are obtained by TWR analysis. The TWR, DAT and KPCA-TWR algorithms are used to analyze and process the temperature evolution data, and the signal-to-noise ratio (SNR) values of the reconstructed feature maps of different methods are compared. The results show that the SNR value of the KPCA-TWR method in the feature map reconstruction is about 130 % higher than that of the traditional TWR method, and about 80 % higher than that of the PCA-TWR method. The contrast between the defect area and the non-defect area is significantly enhanced, thus effectively improving the detection ability of the internal defects of carbon steel materials.