Analysis Of UH-60 L/M Black Hawk Fleet Usage In Support Of A Partial Usage Spectrum Update
Bibliographic record
Abstract
Structural usage and loads monitoring can enhance safety, by identifying unusual usage patterns by individual aircraft or sub-fleets (e.g. operators, missions, or locations), and provide benefit to operators by enabling extended retirement times of life-limited components. While flight regime recognition (RR) algorithms have been demonstrated and partially validated, the use of existing onboard generic RR software provided in legacy Health and Usage Monitoring Systems (HUMS) remains a challenge for achieving airworthiness approval of retirement time extensions using archived fleet data in compliance with existing guidance, such as the U.S. Army ADS-79E Handbook for Condition-Based Maintenance. The U.S. Army and Sikorsky Aircraft, a Lockheed Martin Company, conducted a joint Fatigue Life Management (FLM) project to configure, validate, and apply processes and methods for extending the retirement times of six high-value components for the Army's Black Hawk helicopter fleet, which is equipped with a state-of-the-art HUMS, known as the Integrated Vehicle Health Management System (IVHMS). A RR post-processing process for addressing key technical challenges associated with the use of legacy onboard RR software was configured and verified against existing UH-60 flight test data. These post-processing methods were then applied to a two year population of UH-60 fleet data to calculate usage statistics for individual aircraft within several sub-fleet populations associated with either different global deployment locations and/or missions. The fleet statistics were then used by the Army Aviation Engineering Directorate (AED) to establish a conservative update to the existing usage spectrum, which was applied by Sikorsky to calculate updated retirement times. The focus of this paper is on the successful configuration and verification of RR software used in compliance with ADS-79E to establish UH-60 A/L/M IVHMS fleet usage statistics. A companion paper, also published within proceedings of the AHS 74th Annual Forum, provides details on using the fleet statistics to define an updated UH-60 usage spectrum.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".