Rationalizing scheduled-maintenance requirements using reliability centered maintenance-a Canadian Air Force perspective
Bibliographic record
Abstract
Failure modes and effects analysis (FMEA) and reliability centered maintenance (RCM)/maintenance steering group (MSG) decision logic have been successfully used by military and commercial aviation manufacturers for over three decades to develop preventive maintenance programs for new aircraft fleets. However, once a fleet is in place, there is a requirement to periodically validate or rationalize the applicability and effectiveness of individual tasks in the program, and to adjust task frequencies. Experience has shown that it is inefficient to re-apply FMEA/RCM decision logic to every aircraft item on a fixed frequency basis. This paper identifies how the Canadian Air Force (CAF) proposes to make more efficient and effective use of the in-service data it collects to identify those items for which the preventive maintenance requirement is ineffective or inapplicable. Moreover, it discusses how the same data source can be used in follow-up investigation to determine the actual failure mode history of an item as a basis for comparison with the FMEA-the basis upon which the requirement for the existing tasks is developed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".