Research On Reconstruction Algorithm Of Planar Array Capacitance Image For Defect Detection Of Adhesive Layer

Spacecraft usually bond the heat insulation composite material on the body surface as a thermal protection system to achieve the purpose of heat insulation.For the flight safety of the spacecraft,it is necessary to ensure that the adhesive layer does not contain any defects.In view of the testing requirements of adhesive layer and the complex characteristics of thermal insulation composites,it is necessary to detect the adhesive layer through the thermal insulation material when detecting the adhesive layer.The planar array capacitance imaging technology is suitable for the detection of the defects of the adhesive layer due to its advantages of high sensitivity,non-contact,high detection depth and single-side detection.In order to realize the precise characterization of defects,this paper focuses on in-depth research on improving the accuracy of reconstructed images.The main research contents are carried out in the following aspects:Firstly,when detecting the defects of the adhesive layer,it is necessary to penetrate the thermal insulation composite material to detect whether there are defects in the adhesive layer.Considering the complex nature of the thermal insulation composite material,a planar array capacitiance imaging system is built to detect the defects of the adhesive layer.In addition,considering the "soft field" nature of the sensitive field,the sensitivity imaging accuracy of different layers is different.In order to study the distribution of the sensitive field in different detection depths,the finite element simulation software is used to model the sensitive field,which lays the foundation for subsequent defect detection experiments to improve the accuracy of image reconstruction.Secondly,considering the complex nature of the material and the nonlinearity of the sensitive field,the capacitance data obtained by the system based on weak fringe electric field detection is of a small order of magnitude.Aiming at the problem that the unstable capacitance data would interfere with the reconstructed image,an improved fuzzy c-means clustering algorithm based on particle swarm optimization is proposed to deal with unstable capacitance data so as to improve the accuracy of defect detection.This method not only ensures the validity of capacitance data,but also improves the stability of capacitance data.Defect detection experiments are designed to verify the effectiveness of the proposed algorithm for the optimization of capacitance data.Finally,in view of the low detection accuracy of the adhesive layer defect detection,focusing on the serious ill-posed characteristics of the inverse problem in the process of solving the dielectric constant distribution,a hybrid regularization method is proposed to improve the quality of image reconstruction,thereby improving defect detection accuracy.This method corrects the smaller singular value of the sensitivity matrix and ensures that the larger singular value will not be corrected.At the same time,image reconstruction is performed with the capacitance data optimized by the improved fuzzy c-means clustering algorithm to obtain a higher-quality reconstructed image.In addition,an optimal threshold method is proposed for post-processing of reconstructed images to solve the problem of artifacts in reconstructed images.The optimal threshold is used to transform the reconstructed image into a binary image,so that the artifact is completely eliminated and the accuracy of defect detection is improved.