Validation of a Lamb wave-based structural health monitoring system for aircraft applications
Bibliographic record
Abstract
Structural Health Monitoring technologies have the potential to reduce life-cycle costs and improve reliability for aircraft. Previous research conducted by the Metis Design Corporation has demonstrated the ability of Lamb wave methods to provide reliable information regarding the presence, location and type of damage in coupon-level specimens. Several critical system components have been developed during the course of this research, including circuitry and packaging, and integrated into the Monitoring & Evaluation Technology Integration (M.E.T.I.) Disk. In order to demonstrate the validity of M.E.T.I.-Disks for aircraft applications, a testbed has been fabricated by dividing a 1/8" plate of aircraft-grade aluminum into four equal quadrants with several c-channels. M.E.T.I.-Disk nodes were then placed in the center of each quadrant, and data was collected and interpreted by the METISv2.10 software package. The results produced by this software validated the M.E.T.I.-Disk by using a single undamaged cell to calibrate the system, and then correctly identify that there was no damage present in the remaining quadrants. Next, representative damage was introduced into several combinations of the quadrants, and the software was executed again to query the structure. The resulting data revealed the presence and location of damage, while still identifying the two undamaged regions.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".