Lichten Award Paper: Automated Optical Rotor Blade Tip Clearance Tracking Using Artificial Intelligence Algorithms
Bibliographic record
Abstract
Analytical methods used to determine weapon projectile trajectory relative to helicopter rotor blade tips during most conceivable maneuvers tend to provide insufficient safety margins to accurately define a safe firing envelope. The flight test team at Sikorsky, a Lockheed Martin Company, was given the challenging task of measuring the weapon-to-rotor-blade-tip clearance during these maneuvers. The first-generation methodology was entirely manual using a flat vertical projection screen tangentially aligned with the rotor tip path plane to visualize the intersection between the weapon's projectile and blade tip path. While the first flight test program utilizing this technique was successful in its own right, by the time a second opportune flight test program was initiated, software tools had been developed to help automate the process, drastically increasing the amount of data that could be used to correlate with other aircraft state parameters. During the data processing, a training set was created that was used to build an Artificial Intelligence (AI) capable of performing the same task while bringing the processing time down from 4-5 frames per second (fps) to 130 fps. This enables real-time AI inference to be taken from digital video cameras on the helicopter and clearance measurements real-time processed and recorded to the instrumentation system or displayed for the pilot's awareness.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".