Towards Trustworthy AI Solutions in Future Aircraft: The Case of Arc Fault Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aeronautical industry demands high redundancy and security from its embedded devices. The increasing electrification in the sector, due to the replacement of hydraulic and pneumatic systems by electrical ones, makes old problems more dangerous, such as arc faults. Detecting them is more difficult when they are in series with the loads and subjected to direct current and voltage. Due to their critical nature, the protection systems must be highly effective. Today there are no certification procedures for embedding artificial intelligence algorithms, therefore a methodology for robustness verification is presented, ensuring the stability of their response in presence of adversarial disturbances. Through the results obtained, this work demonstrates that arc fault recognition algorithms considering adversarial examples are better compared to those that do not foresee such situations. The final performance reaches 99% accuracy for the most robust trained models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it