Joint Military and Commercial Rotorcraft Mechanical Diagnostics Gap Analysis
Bibliographic record
Abstract
A group of rotorcraft original equipment manufacturers (OEMs) and military and commercial operators have come together to review the current state of mechanical diagnostics (MD) for on board rotorcraft Health and Usage Monitoring Systems (HUMS). HUMS has become an integral part of the modern rotorcraft both in commercial and military operations to enhance safety and enable Condition-Based Maintenance (CBM). Commercial oil and gas operators depend on the HUMS vibration monitoring and MD to comply with regulations and customer requirements for ensured safety of off-shore transportation. Under the auspices of the HUMS Technical Committee within the American Helicopter Society (AHS), the authors have assessed the performance of HUMS MD through both quantitative and qualitative means. First, results from the U.S. Army fleet, which comprises thousands of deployed HUMS on multiple aircraft models, were examined. Second, qualitative surveys of both commercial/military operators and rotorcraft/HUMS original equipment manufacturers (OEMs) were completed. Finally, a literature survey focused on HUMS research and development (R&D) and operational analysis was conducted. Based on this assessment, gaps in the performance of current HUMS MD, needs for future R&D, and challenges to closing those gaps are identified. Collaborative, pre-competitive efforts are also recommended to help close the gaps and generally raise the performance of HUMS MD to enable further enhancements to safety and to support expanded CBM initiatives.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".