Machine learning-based feature selection of microwave testing signals and visualised evaluation of backside wall-thinning defects in polyethylene components

Abstract

In virtue of the exceptional material characteristics, Polyethylene (PE) components are broadly utilised in various engineering applications such as pressure-rated gas, water systems and sustainable energy systems, etc. However, during practical applications, PE components could be vulnerable to such damages as the Backside Wall-thinning Defect (BWD) which is resulted from the improper installation process or lateral impact, etc. The structural integrity and safety of PE components are severely threatened by the defect. Therefore, efficient testing, imaging and evaluation of BWDs are highly demanded for non-intrusive inspection of PE components. Complementary to other non-destructive evaluation techniques, Microwave Testing (MWT) has been found to be superior in the inspection of dielectric structures. In light of this, in this paper the visualised evaluation of BWDs in the planar PE component via the Ka-band MWT is intensively scrutinised. In an effort to evaluate the planar dimension and depth of a BWD, the 3D profiling of the BWD based on the U-Net is investigated together with the multiple hypothesis test-based signal-feature selection. The feasibility and applicability of MWT, along with Ka-band (26.5 ∼ 40 GHz) microwave reflectometry and the proposed algorithms, for the detection, visualisation, and assessment of BWDs in PE components are demonstrated by the experimental results.