<title>Role of data fusion in NDE for aging aircraft</title>
Bibliographic record
Abstract
As structural integrity models for aging aircraft begin to include the effects of corrosion and corrosion-fatigue, nondestructive evaluation (NDE) techniques will be called upon to provide metrics of corrosion for input to these models. It is unlikely that any one NDE technique can provide all the required metrics to characterize the condition of complex airframe structures. This paper discusses how data fusion can be used to integrate the results of multiple NDE techniques into a form suitable for input to structural models. Examples of the inspection of service-retired lap joints with NDE techniques including pulsed eddy current, conventional eddy current, Edge of Light, and D Sight are given. Significant metrics for structural models of the joint are discussed, and the performance of the individual NDE techniques on the metrics of corrosion and fatigue is evaluated. The results are used to generate a set of requirements for data fusion system to successfully transform the NDE data to a form a suitable for input to the structural models.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".