Reconstruction Algorithm And Experimental Research Of Thermal Insulation Defect Detection Based On Planar Capacitance Imaging

As an innovative non-destructive testing method,planar array capacitance imaging technology has been widely concerned at home and abroad.It not only has the characteristics of non-contact,fast response and high measurement accuracy of electrical capacitance tomography.At the same time,planar array capacitance imaging technology is not limited by the material characteristics and use space of the tested object,and can be used without any coupling medium.The realization of nondestructive testing of the object under test has greatly opened up the application field of this technology,and provided a new technical means for the defect detection of industry.In order to improve the detection accuracy of planar array capacitance imaging technology,this paper makes an in-depth study on image reconstruction algorithm,and its main research contents are as follows:First of all,in order to solve the problem that the planar array capacitance imaging system is vulnerable to the influence of external environment and hardware conditions,we use a method of clustering and optimizing the capacitance data in advance.According to the characteristics of the capacitance data of the plane array electrode,the convergence of the fuzzy clustering optimization algorithm is used to realize the data optimization.The experimental results show that the optimization algorithm proposed in the image reconstruction results can obtain more accurate and effective capacitance data,and the final image reconstruction accuracy is greatly improved compared with the data before optimization.Then,In order to further reduce the interference of the external environment on the measured capacitance value,this paper proposes an improved wavelet filtering imaging method.In this method,the threshold selection method is improved first,and then the capacitance data is processed by the wavelet filtering algorithm of the improved threshold selection method,so as to reduce the impact of noise interference while the effective data is kept unchanged.Finally,the experiment of defect detection is designed.Through the comparative analysis of the capacitance data before and after optimization and the results of image reconstruction,the superiority of the image processing method based on the improved wavelet filter is verified.Finally,in order to improve the accuracy of image reconstruction and reduce the error of image reconstruction,this paper proposes a new image fusion algorithm based on the existing wavelet fusion algorithm,which combines the regional feature measurement fusion rule and weighted average fusion rule.This method can reduce the noise interference of image and fuse the defective area information in the results of various imaging algorithms.Through the simulation experiment,the accuracy of the final reconstruction image has been greatly improved,which verifies the superiority of this method in image reconstruction.