Study of Lift-Off Invariance for Pulsed Eddy-Current Signals
Bibliographic record
Abstract
Lift-off invariance (LOI) is getting much attention from researchers in the field of electromagnetic nondestructive evaluation (ENDE) because, at the LOI point, eddy-current signals for different lift-offs intersect and the signal amplitude is independent of the lift-off variance. We discuss our ongoing research into LOI, starting from an overview of the state-of-the art of pulsed eddy-current testing (PEC) systems and their use in the elimination of the lift-off effect. We have investigated LOI characteristics with respect to variation in the configuration of the PEC probe in a theoretical study, implemented by extended truncated region eigenfunction expansion (ETREE) modeling. We found that: 1) the LOI occurs when the first-order time derivative of the magnetic field signals are acquired from Hall sensors; 2) an LOI range instead of a single LOI point, when multiple lift-offs are introduced through both experimental and theoretical studies. The LOI range varies with the Hall sensor position in the probe assembly and the conductivity of the samples under inspection, which are important parameters for the design and development of PEC systems. Based on this understanding, we investigated new approaches using theoretical computation and multiple lift-off, or magnetic sensor arrays, for conductivity and lift-off estimation. Our study can be extended for design and development of multipurpose eddy-current sensor systems for surface form measurement and defect detection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".