An Integrated Method for Fully Covered Back Crack Defect Detection on Steel Plate Under Variable Lift Off

Crack and corrosion defects commonly occur on pipeline walls under stress and erosion, with back cracks often fully covered by surface corrosion due to size differences. The detection of the covered back crack defects is challenging under variable lift off because of signal shielding and interference, increasing pipeline fracture risks. To address this, integrated methods with lift off compensation are widely used to back crack defect detection under variable lift off. However, current methods fail to accurately detect fully covered back crack defects under variable lift off, as they cannot break through surface corrosion coverage or meet the timeliness of lift off compensation. To solve the above problems, an integrated method is proposed to detect the fully covered back crack defects under variable lift off. First, a mutually supervised bypass strategy is proposed to detect back crack defects by bypassing the coverage of surface corrosion defects and to prevent missed or false detections. Then, an integrated structure of two EMATs and one ECT is proposed to synchronously acquire defect and lift off information by sharing excitation, which ensures the timeliness of lift off compensation. Next, based on the ECT, EMAT1, and EMAT2 signals, a quantification method using the lift off compensation element is proposed to quantify the depth of back crack defects under variable lift off. Finally, the experimental results indicate that the proposed integrated method can accurately detect the fully covered back crack defects under variable lift off.

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Publication Details

Journal
IEEE Transactions on Industrial Electronics
Published
2025-06-18
DOI
https://doi.org/10.1109/tie.2025.3574394
Citations
1
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
1.24
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article

An Integrated Method for Fully Covered Back Crack Defect Detection on Steel Plate Under Variable Lift Off

Jiayue Sun, Hang Xu, Qiangxin Li, Lin Jiang et al.
1 citations
IEEE Transactions on Industrial Electronics
Industrial Vision Systems and Defect Detection
1.24
article

An Integrated Method for Fully Covered Back Crack Defect Detection on Steel Plate Under Variable Lift Off

Jiayue Sun, Hang Xu, Qiangxin Li, Lin Jiang, Huaguang Zhang, Jinhai Liu
article en
1 citations

Abstract

Crack and corrosion defects commonly occur on pipeline walls under stress and erosion, with back cracks often fully covered by surface corrosion due to size differences. The detection of the covered back crack defects is challenging under variable lift off because of signal shielding and interference, increasing pipeline fracture risks. To address this, integrated methods with lift off compensation are widely used to back crack defect detection under variable lift off. However, current methods fail to accurately detect fully covered back crack defects under variable lift off, as they cannot break through surface corrosion coverage or meet the timeliness of lift off compensation. To solve the above problems, an integrated method is proposed to detect the fully covered back crack defects under variable lift off. First, a mutually supervised bypass strategy is proposed to detect back crack defects by bypassing the coverage of surface corrosion defects and to prevent missed or false detections. Then, an integrated structure of two EMATs and one ECT is proposed to synchronously acquire defect and lift off information by sharing excitation, which ensures the timeliness of lift off compensation. Next, based on the ECT, EMAT1, and EMAT2 signals, a quantification method using the lift off compensation element is proposed to quantify the depth of back crack defects under variable lift off. Finally, the experimental results indicate that the proposed integrated method can accurately detect the fully covered back crack defects under variable lift off.

IEEE Transactions on Industrial ElectronicsVol. 72(12)
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University (CN)
Sustainable cities and communities
Openalex Percentile: Top 18%
Industrial Vision Systems and Defect Detection
1.24
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