Flight Testing of Health Management Technologies
Bibliographic record
Abstract
The U.S. Army traditionally has used a time-based, on-condition maintenance paradigm that relies on at-aircraft inspections and periodic in-depth phased inspections to determine condition and ensure airworthiness. The result is a significant maintenance burden, both scheduled and unscheduled, and excessive aircraft downtime. The objective of the Aviation Development Directorate (ADD) and Sikorsky Aircraft Corporation (SAC) Capability-Based Operations and Sustainment Technology-Aviation (COST-A) program was to develop and demonstrate an integrated set of high value diagnostics, prognostics, and system health management technologies that reduce scheduled inspections and preventive maintenance while enhancing safety. More than two dozen Prognostics and Health Management (PHM) technologies across six primary rotorcraft systems (propulsion, drive train, airframe/structural, rotor, electrical, and vehicle management) were matured to technology readiness level (TRL) 6. These technologies were integrated into a prototype laboratory on-board system built around the Integrated Vehicle Health Management Unit (IVHMU) currently installed in all UH-60 Black Hawk aircraft and successfully demonstrated to perform concurrently in representative simulated flight scenarios, using playback data from healthy and faulty components. A subset of these technologies, jointly selected by ADD and SAC, was flight tested on an HH-60M aircraft to further reduce the risk of transitioning these technologies. This paper summarizes the flight-test efforts, with a focus on results obtained for the technologies under test. Upon deployment to the UH-60 aircraft fleet, these PHM technologies can enable the Army to transition to a more effective automated condition-based maintenance (CBM) paradigm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".