A Dynamic Imaging Algorithm for Defect Detection Based on Planar Electrical Capacitance Tomography

Abstract

Planar electrical capacitance tomography(PECT) is a promising nondestructive testing (NDT) method. Currently, PECT faces many challenges in large-area component defect detection. The main ones are the low time efficiency and inability to visualize of existing static imaging techniques and dynamic detection methods with a single pair of electrodes, which are especially ineffective in the detection of large areas or complex structures. To solve these problems, a dynamic fusion imaging algorithm for array electrode sensors is proposed in this paper to realize the defect detection capability of large-area components. Firstly, a defect size determination method based on capacitance data is proposed to improve the accuracy of detection. Secondly, the idea of image fusion is introduced to ensure the reconstructed image integrity and solve the difficult matching of data features problem. Thirdly, the multi-objective threshold programming method is adopted for image progress. Moreover, to solve the background and target boundary segmentation blurring problem, an optimized 2D Otsu threshold-solving method is proposed. Finally, considering the problem of high computational complexity and low efficiency in threshold solving, the MOEA/D evolutionary algorithm based on the dual operator strategy is proposed. The proposed dynamic imaging algorithm is experimentally verified capable of visualizing internal defects in large-area materials/components.

Keywords: Dynamic imaging；Defect detection；Planar electrical capacitance tomography；Evolutionary algorithms ；Data feature