A Scoring Approach to Assess Maintenance Risk for Aircraft Systems in Conceptual Design
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3881.vid Ease of maintenance can significantly contribute to reducing aircraft operational cost. Maintenance risk is defined as the opposite of maintenance ease; it is impacted by many factors, most of which are decided upon during the aircraft's conceptual design. This paper proposes a novel method to assess the maintainability risks of aircraft systems by combining various aspects on the component level, intercomponent level, the bay level and the aircraft level. For each level, all contributing factors to maintenance risk are analyzed and integrated into several scores. These maintenance risk scores can be used to assess the various aspects contributing to maintenance risk, using input parameters available during the conceptual design phase. This paper presents the validation of the scores using different components and aircraft equipment bays, such as avionic racks, the nose cone and a complex aft-equipment bay of a business jet. The proposed maintenance risk scoring method will enhance multidisciplinary trade-off studies during aircraft conceptual design, considering competing design aspects such as system placement, thermal aspects, impact on aircraft balancing or even the overall aircraft shape. Therefore, this new conceptual design capability enables designing novel aircraft configurations featuring unconventional system component placement or component bay shapes while considering maintenance aspects upfront.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".