Maintenance, Engineering, and Operational Decision-Making Metrics Derived from Simple Maintenance and Aircraft Datasets
Bibliographic record
Abstract
Using in-service maintenance data, it is possible to predict and forecast the propulsion system contribution to aircraft and fleet level unavailability and to identify sub-system degraders of overall engine reliability. While more complex means of assessing reliability exist, increased layers of complexity can lead to increasing difficulty when used to convince a military commander or fleet manager of the appropriate action to take. Furthermore, increased complexity increases the time required to produce, to analyze, and to assess results of reliability assessments. In a time-critical situation, when faced with the need for an immediate maintenance or engineering decision, the best information is that which is the simplest and easiest to understand, quickest to produce, and fastest to apply. In this work, a minimum list of data requirements will be developed with an associated means of analyzing these data to produce meaningful indicators to predict and to forecast unavailability and mission abort rates that can be used to plan for deployed or sustained operations. Analysis of the same data set can produce a prioritized listing of sub-system reliability degraders to drive engineering decisions for component improvement. The Royal Canadian Air Force’s CT114 Tutor aircraft will be the basis for analysis demonstrating that sophisticated sensors and data systems are not required to be able to produce meaningful data suitable for significant fleet level decisions. Statistical methods and appropriate data filtering were applied to the engine system to derive rates for overall mission aborts, aircraft unavailability and aircraft unreliability for the top sub-system degraders. Conclusions drawn include that this information, if calculated correctly, can provide decision makers with the critical information required to make significant fleet wide decisions. Recommendations and methodology are presented that are applicable to any military or civil aircraft fleet at the sub-system, aircraft, and fleet level.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".