Machine-learning-based evaluation of corrosion under insulation in ferromagnetic structures

Corrosion under insulation CUI is one of the challenging problems in pipelines used in the gas and oil industry as it is hidden and difficult to detect but can cause catastrophic accidents. Pulsed eddy current (PEC) techniques have been identified to be an effective non-destructive testing (NDT) method for both detecting and quantifying CUI. The PEC signal’s decay properties are generally used in the detection and quantification of CUI. Unfortunately, the well-known inhomogeneity of the pipe material’s properties and the presence of both cladding and insulation lead to signal variation that reduces the effectiveness of the measurement. Current PEC techniques typically use signal averaging in order to improve the signal-to-noise ratio (SNR), with the drawback of significantly-increasing inspection time. In this study, the use of Gaussian process regression (GPR) for predicting the thickness of mild carbon steel plates has been proposed and investigated with no signal averaging used. With mean absolute errors (MAE) of 0.21 mm, results show that the use of GPR provides more accurate predictions compared to the use of the decay coefficient, whose averaged MAE is 0.36 mm. This result suggests that the GPR-based method can potentially be used in PEC NDT applications that require fast scanning.