Methodology for the automated preliminary certification of on-board systems architectures through requirements analysis
Bibliographic record
Abstract
Aircraft on-board systems architectures are defined by the subsystems and the connections among them. The requisites for these connections are not directly established in the certification specifications but they are indi- rectly derived from other requirements. In addition, generally only a small number of architectures taken from previous studies are considered when performing on-board systems design. This makes it difficult to generate certifiable connections when assessing an extensive number of architectures. Considering certification aspects during early design stages can be used as a filter to save computational time by calculating only potentially certifiable architectures. The aim of this paper is to develop a methodology to automatically assess certifi- cation requirements of on-board systems architectures that come from the certification specifications. One part of the methodology consists of a list of requirements to be considered to define the connections among on-board systems during architecture design in order to find safe and certifiable solutions. The other part is focused on the automation of the reliability block diagram technique. This is needed in order to verify safety assessment requirements which have a high influence on the architectures and connections. The advantages of this study are mainly the capability to assess multiple architectures and to verify certification requirements during early design stages. A full automation for this process was achieved and showed through an example test case. An aeronautical application case is also shown. This analysis could also be implemented for the study of innovative on-board systems architectures.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".