High Precision Defect Sizing Method for Capacitive Imaging Based on Physics-Informed Neural Network

Nonconductive materials are extensively used in industrial applications, particularly as coatings for metal structures like oil pipelines. However, these nonmetallic coatings are prone to damage from factors, such as corrosion and scratches, leading to widespread failures. This increases the demand for nondestructive evaluation techniques capable of accurately quantifying defect parameters in such materials. Capacitive Imaging (CI) technique is an emerging electromagnetic nondestructive testing method with promising application prospects in defect evaluation in nonconducting materials. However, the CI technique is commonly used as a screening technique to detect the presence of possible defects, and its defect sizing ability, which is crucial in some engineering applications, has yet to be explored. This article proposes a high precision defect sizing method for the CI technique based on a physics informed neural network. First, the physical model of the CI technique for the detection of defects in nonconducting material is analyzed. A physical formula, which was later used as physical information, for the quantification of defect length and width was then obtained. Finite-element simulations were then conducted to visualize the sensitivity distribution of the CI sensor and analyze the characteristics of defect signals the physical information was integrated into a neural network, enabling it to quantify defect parameters from the CI detection data. Experimental results demonstrate that this method can accurately determine defect length, width, depth, and buried depth. Compared to other neural network structures and traditional algorithms, the proposed approach achieves superior precision in defect quantification.