Progress Towards Autonomous Structural Health Management
Bibliographic record
Abstract
The Autonomous Sustainment Technologies for Rotorcraft Operations-Structures (ASTRO-S) project between U. S. Army Combat Capability Missile Center, Aviation Development Directorate-Eustis (FCDD-AMV-E) and Sikorsky developed and validated a range of technologies to enable reduced airframe maintenance burden, increase operational availability, and provide key enabling technologies relative to Army's transition to the new paradigm of Maintenance Free Operational Periods (MFOP) for the rotorcraft of the future. Methods were developed for autonomous characterization of major damage and residual strength expressed as a Structural Health Index (SHI) for advanced durable and damage tolerant composite aerospace structural assemblies with redundant load paths, enabling targeted inspections and strength-based fly / watch / repair decisions. A number of sensing technologies including fiber-optic strain measurement and piezo-based structural health assessment, along with a number of innovative advanced algorithms that intelligently use changes in monitored structural responses, were implemented in a comprehensive architecture to detect, localize, and assess the severity of structural damage. Extensive testing on full-scale, multiload-path composite structures to assess feasibility and effectiveness of the developed technologies, as well as understand application and transition challenges, has convincingly shown that damage detection, localization, and severity assessment in an autonomous fashion is feasible. Further, it was shown that the concept of a trendable SHI to assess residual strength, is viable, although additional full-scale test cases are needed to further validate and mature the approach. Overall, these key findings affirm suitability of the technical approach and associated algorithms for reducing maintenance burden by triggering rather than scheduling inspections and potentially deferring repairs in high op-tempo environments. These structural health management technologies will be key enablers supporting Army's future rotorcraft when operating in an untethered multi-domain battle space.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".