A Microwave Time Domain Reflectometry Technique Combining the Wavelet Decomposition Analysis and Artificial Neural Network for Detection of Defects in Dielectric Structures

Abstract

The microwave nondestructive testing (MNDT) technique is an attractive candidate for defect detection in the dielectric structures as microwave signals can penetrate inside and interact with the insides of the dielectric material. In this research, a novel MNDT technique is presented for defect detection combining the time domain reflectometry (TDR) technique, the wavelet decomposition analysis (WDA) technique, and an artificial neural network (ANN), which has good generality and can be applied to various dielectric materials with different thicknesses. This method is based on scanning the dielectric material with an open-ended rectangular waveguide (OERW) operating from 18 to 26.5 GHz with 201 frequency points and analyzing its reflections using the proposed algorithm. The method was validated by simulation and experiments successfully. In the simulations, two typical kinds of dielectric materials were used to verify the generality and accuracy of this proposed method. The accuracies of these two dielectric materials were 96% and 90%, respectively. A 2-D image of detection can be constructed by performing repetition of the process with a certain step on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xy</i> plane of a material, which is illustrated by simulations. Meanwhile, the sensitivity analysis was performed by studying the similarity quantified using the Pearson’s coefficient correlation. Eventually, some FR4 samples were machined to demonstrate the feasibility of this method. The acceptable accuracy is 86% of the experimental result.