Employing Legacy HUMS Data to Support Aging Aircraft ASIP
Bibliographic record
Abstract
While Health and Usage Monitoring Systems (HUMS) continue to advance and provide increasingly refined Condition Based Maintenance (CBM) solutions (exemplified at References 1 through 6), HUMS-equipped aircraft procured before the maturation of these systems require consideration of novel approaches to maximize the benefit of available data. Presented as a case study, the Canadian Search and Rescue CH149 Cormorant helicopter, a variant of the Finmeccanica (AgustaWestland) EH-101, entered service for Canada in 2002 equipped with a HUMS. The following paper will present how HUMS data have been employed to support the CH149 fleet of 14 aircraft; specifically, how such data are used to (i) support fleet and individual aircraft usage monitoring, and (ii) identify adverse trends for additional detailed investigation. It is herein demonstrated that heavily processed data from a legacy HUMS, even without defined thresholds, may have value with proper consideration and validation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".