Digital Data Standards in Aircraft Asset Lifecycle: Current Status and Future Needs
Bibliographic record
Abstract
The aerospace ecosystem is a complex system of systems comprising of many stakeholders in exchanging technical, design, development, certification, operational, and maintenance data across the different lifecycle stages of an aircraft from concept, engineering, manufacturing, operations, and maintenance to its disposal. Many standards have been developed to standardize and improve the effectiveness, efficiency, and security of the data transfer processes in the aerospace ecosystem. There are still challenges in data transfer due to the lack of standards in certain areas and lack of awareness and implementation of some standards. G-31 standards committee of SAE International has conducted a study on the available digital data standards in aircraft asset life cycle to understand the current and future landscapes of the needed digital data standards and identify gaps. This technical paper presents the study conducted by the G-31 technical committee. This paper reviews the data being exchanged between various stakeholders in the aerospace asset lifecycle and the availability of standards for the data transfer within the aerospace ecosystem. It identifies gaps based on the list of currently available data standards, and then creates a future landscape to address the needed digital data standards. This paper focuses on aircraft operations, maintenance, transfer, disposal processes, and post-build stage, and does not address the detailed interactions during the aircraft design, development and manufacturing phases. Its scope is also limited to key stakeholder interactions throughout the different stages of the aircraft operations, maintenance, and retirement.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".