Understanding Magnetic Flux Leakage Signals From Gouges
Bibliographic record
Abstract
The Magnetic Flux Leakage (MFL) technique is sensitive both to pipe wall geometry and pipe wall strain, therefore MFL inspection tools have the potential to locate and characterize mechanical damage in pipelines. The present work is the first stage of a study focused on developing an understanding of how MFL signals arise from pipeline gouges. A defect set of 10 gouges were introduced into sections of 12″diameter, 5m long, end capped and pressurized X60 grade pipe sections. The gouging tool displacement ranged (before tool removal) between 2.5 to 12.5mm. Gouges were approximately 50mm in length. The shallowest indentation created only a very slight scratch on the pipe surface, the deepest created a very significant gouge. All gouges were axially oriented. Experimental MFL measurements were made on the external pipe wall surface (pressurized) as well as the internal surface (unpressurized). The early results of the experimental MFL studies, and a hypothesis for the origin of the MFLaxial signal “dipole” are discussed in this paper.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".