Aircraft electric system intermittent arc fault detection and location
Bibliographic record
Abstract
Intermittent arc faults appear in aircraft power systems in unpredictable ways when the degraded wires are wet, vibrating against metal structures, or under mechanical stresses. They could evolve into serious faults that may cause onboard fires, power interruptions, system damage and catastrophic incidents, and thus have raised concern. Recent trends in solid state power controllers (SSPCs) motivated the development of nondestructive diagnostic methods for health monitoring of aircraft wiring. In this paper, the ABCD matrix (or transmission matrix) modeling method is introduced to derive normal and faulty load circuit models with better accuracy and reduced complexity compared to the differential equation approach, and an intermittent arc fault detection method is proposed based on temporary deviations of load circuit model coefficients and wiring parameters. Furthermore, based on the faulty wiring model, a genetic algorithm is proposed to estimate the fault-related wiring parameters such as intermittent arc location and average intermittent arc resistance. The proposed method can be applied to both the alternating current power distribution system (PDS) and the direct current (DC) PDS. Experiments using a DC power source have been conducted, and the experimental results have demonstrated effectiveness of the proposed method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".