Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

Abstract

Corrosion under insulations is described as localized corrosion that forms because of moisture penetration through the insulation materials or due to contaminants’ presence within the insulation material. The traditional non-destructive inspection techniques operating at a low frequency require removing insulation material to enable inspection, due to poor signal penetration. Several high-frequency inspection techniques such as the microwave technique have shown successful inspection in detecting the defect under insulations, without removing the insulations. However, the microwave technique faces several challenges such as poor spatial imaging, large errors in terms of defect size and depth owing to stand-off distance variations, optimal frequency point selection, and the presence of the outlier in microwave measurement data. The microwave technique in conjunction with machine learning approaches has tremendous potential and viability for assessing corrosion under insulation. Machine learning is highly possible to improve the efficiency of microwave techniques because its impact has been demonstrated in various conventional non-destructive techniques. This paper provides an in-depth review of non-destructive techniques for assessing corrosion under insulation, as well as the possibility of using machine learning approaches in microwave techniques in comparison to other conventional techniques.