Achieving HUMS Program Financial Benefits - 12 years of day to day HUMS operations on the CH-146 Griffon fleet
Bibliographic record
Abstract
Over the years, the cost/benefit balance of Health & Usage Monitoring Systems (HUMS) available for rotorcraft have generated much debate, especially at the procurement phase of new aircraft. Currently, there is consensus within and outside the HUMS community regarding the safety benefits of having a HUMS system installed. The Royal Canadian Air Force worked in conjunction with the HUMS support team from Bell Helicopter Canada to establish the direct quantitative cost savings of running the HUMS program on their fleet of 85 CH-146 (Bell 412CF) Griffon helicopters. Accepting the safety benefits, the objective was to determine whether the cost savings generated by the program would offset the annual investment required. Using the HUMS database and the various military tracking systems, the number of events for which HUMS provided sufficient data to deviate from either a maintenance action or from a conditional inspection was compiled for a 5 year period from the beginning of 2008 to the end 2012. The total saving was conservatively estimated at $2.1M per year which definitively surpasses the annual investment required to keep the program running.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".