Enhancement of Defect Characterization With AC Magnetic Flux Leakage: Far-Side Defect Shape Estimation and Sensor Lift-Off Compensation
Bibliographic record
Abstract
One of the most common methods for performing non-destructive testing (NDT) of the steel tank floors in aboveground storage tanks is dc magnetic flux leakage (MFL). This test method gives an estimate of the defect depth and width based on the MFL signal strength and its peak location, respectively. A key limitation is that the signal strength depends on not only the defect depth and width but also various other factors including a defect’s wall profile and sensor lift-off. Moreover, in a practical MFL test, the sensor lift-off changes due to surface roughness and uneven steel plate surface in tank floors. We present an ac MFL system to increase the accuracy of defect characterization by: 1) distinguishing between two common defect shapes located on the far side of the steel plate used in a typical above storage tank (AST) floor: lake-shaped and rectangular defects and 2) developing a sensor lift-off compensation scheme based on ac signal phase. Simulation results show that skewness of the ac MFL can be used to distinguish between far-side lake-shaped and rectangular defects. AC MFL signal phase is shown to be a suitable compensator for the dependence of signal strength on unintended variations in sensor lift-off. The simulation results have been validated through experiments.
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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 | 0.000 |
| 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".