Reconstruction of 3-D pipeline corrosion defect profile from MFL signals with deep learning approach
Bibliographic record
Abstract
Magnetic flux leakage (MFL) is a widely used nondestructive testing technique in pipeline inspection to detect and quantify defects. In pipeline integrity management, the reconstruction of defects from MFL signals plays a critical role in failure pressure prediction and maintenance decision-making. In current research practices, this reconstruction primarily involves the determination of defect dimensions, including length, width, and depth, collectively forming a rectangular box. However, this box-based representation potentially leads to conservative assessments of pipeline integrity. To fine-scale the reconstruction results and provide detailed defect information for the integrity assessment, a 3-D reconstruction model for pipeline corrosion defects from MFL signals is proposed. In detail, the deep neural network is established to capture the nonlinear relationship between the MFL signals and 3-D defect profiles. In contrast to the limited insights offered by the box profile, the reconstructed 3-D profile in this paper enables more detailed metal loss geometry. The experiments using field pipeline in-line inspection data demonstrate promising results on both morphology and depth prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".