Abstract WINSAS: A New Tool for Enhancing the Performance of Eddy Current Inspection of Aging Aircraft Wheels
Bibliographic record
Abstract
Eddy current techniques are widely used in the inspection of aging aircraft. A commercial eddy current system manufactured by ANDEC is currently in use in several airline companies including: Northwest, USAir, Canadian, Lufthansa and Delta airlines. In operating this system, the eddy current probe is moved vertically while the wheel is rotated horizontally, resulting in a helical scan of the wheel outer surface. Two probe types: high and a low frequency probes, are used simultaneously to allow detection of surface and subsurface cracks. A new tool: Wheel Inspection & Signal Analysis System (WINSAS), version 1.0 has been developed and integrated into ANDEC system. WINSAS runs on a PC with Input/Output card and controls the functions of data acquisition & storage, and signal display & analysis. Data is acquired of the impedance channels, coming out of an eddyscope at time intervals determined by an encoder installed on the shaft. Use of the encoder synchronizes the data acquisition process and makes the data display and processing invariant to variations in the shaft speed. WINSAS offers robust handling and interpretation of the eddy current signal. Visualization of the eddy current signal is enhanced using several display forms: electronic strip chart, complex impedance plane, A-scan, and C-scan images. Signal interpretation is also enhanced using WINSAS utilities such as user control of the image colormap and adjustment of the vertical and horizontal A-scan track positions. Automatic classification of the eddy current signal is available in WINSAS based on a neural network approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".