MACHINE-LEARNING-BASED EVALUATION OF CORROSION UNDER INSULATION IN FERROMAGNETIC STRUCTURES

Authors

DOI:

https://doi.org/10.31436/iiumej.v22i2.1692

Keywords:

corrosion under insulation, pulsed eddy current, non-destructive testing, machine learning

Abstract

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.

ABSTRAK: Hakisan di bawah penebat CUI adalah salah satu masalah yang mencabar dalam saluran paip yang digunakan dalam industri gas dan minyak kerana tersembunyi dan sukar dikesan tetapi boleh menyebabkan bencana. Teknik Pulsed eddy current (PEC) telah dikenal pasti sebagai kaedah ujian bukan pemusnah yang berkesan (NDT) untuk mengesan dan mengukur CUI. Sifat kerosakan isyarat PEC umumnya digunakan dalam pengesanan dan pengukuran CUI. Malangnya, sifat tidak tepat yang terkenal dari sifat bahan paip dan kehadiran pelapisan dan penebat menyebabkan variasi isyarat yang mengurangkan keberkesanan pengukuran. Teknik PEC semasa biasanya menggunakan rata-rata isyarat untuk meningkatkan nisbah isyarat-ke-kebisingan (SNR), dengan kelemahan peningkatan masa pemeriksaan dengan ketara. Dalam kajian ini, penggunaan regresi proses Gauss (GPR) untuk meramalkan ketebalan plat keluli karbon ringan telah diusulkan dan diselidiki dan tidak ada rata-rata isyarat yang digunakan. Dengan ralat mutlak (MAE) 0,21 mm, hasil menunjukkan bahawa penggunaan GPR memberikan ramalan yang lebih tepat dibandingkan dengan penggunaan pekali peluruhan, yang rata-rata MAE adalah 0,36 mm. Hasil ini menunjukkan bahawa kaedah berasaskan GPR berpotensi digunakan dalam aplikasi PEC NDT yang memerlukan pengimbasan pantas.

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References

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Published

2021-07-04

How to Cite

Sophian, A., Nafiah, F., Gunawan, T. S., MOHD YUSOF, N. A. ., & Al-Kelabi, A. . (2021). MACHINE-LEARNING-BASED EVALUATION OF CORROSION UNDER INSULATION IN FERROMAGNETIC STRUCTURES. IIUM Engineering Journal, 22(2), 226–233. https://doi.org/10.31436/iiumej.v22i2.1692

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Electrical, Computer and Communications Engineering