Enhancing pulsed eddy current for inspection of P-3 Orion lap-joint structures
Bibliographic record
Abstract
During flight, aircraft are subjected to cyclic loading. In the Lockheed P-3 Orion airframe, this cyclic loading can lead to development of fatigue cracks at steel fastener locations in the top and second layers of aluminum wing skin lap-joints. An inspection method that is capable of detecting these cracks, without fastener removal, is desirable as this can minimize aircraft downtime, while subsequently reducing the risk of collateral damage. The ability to detect second layer cracks has been demonstrated using a Pulsed Eddy Current (PEC) probe design that utilizes the ferrous fastener as a flux conduit. This allows for deeper penetration of flux into the lap-joint second layer and consequently, sensitivity to the presence of cracks. Differential pick-up coil pairs are used to sense the eddy current response due to the presence of a crack. The differential signal obtained from pick-up coils on opposing sides of the fastener is analyzed using a Modified Principal Components Analysis (MPCA). This is followed by a cluster analysis of the resulting MPCA scores to separate fastener locations with cracks from those without. Probe design features, data acquisition system parameters and signal post-processing can each have a strong impact on crack detection. Physical probe configurations and signal analysis processes, used to enhance the PEC system for detection of cracks in P-3 Orion lap-joint structures, are investigated and an enhanced probe design is identified.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".