Prospect of using machine learning-based microwave nondestructive testing technique for corrosion under insulation: A review

Akbar, Muhammad Firdaus and Mohammed Mohsen Shrifan, Nawaf Hassan and Al Gburi, Ahmed Jamal Abdullah and Tan, Shin Yee and Mat Isa, Nor Ashidi and Akbar, Muhammad Firdaus (2022) Prospect of using machine learning-based microwave nondestructive testing technique for corrosion under insulation: A review. IEEE Access, 10. pp. 88191-88210. ISSN 2169-3536

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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. 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.

Item Type:	Article
Uncontrolled Keywords:	Corrosion under insulation, Machine learning-based technique, Microwave non-destructive testing
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	16 Jan 2024 10:39
Last Modified:	16 Jan 2024 10:39
URI:	http://eprints.utem.edu.my/id/eprint/27034
Statistic Details:	View Download Statistic

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