A Testing Framework for Investigating Surface Arcing on High Voltage Insulation Systems for Electrified Aircraft Applications
Bibliographic record
Abstract
This paper investigates the surface arcing failures on high voltage insulation systems used in electrified aircraft applications, through a proposed testing framework. The framework facilitates the creation of surface arcing under controlled atmospheric pressure conditions, with a capability to test under either AC or DC voltage modes. A high frequency current transformer (HFCT) is also utilized to capture surface discharges (SD) prior to arcing as part of the test. At a given fixed electrode distance, the obtained results suggest earlier inception voltages for both the SD and arcing with decreasing pressures mimicking increasing altitudes. Moreover, the difference in the inception voltage between the SD and arcing diminishes with decreasing pressure, until the difference becomes negligible at pressures corresponding to 4 km above sea level, thereby suggesting an increased risk of using SD detection as means to foresee subsequent arcing. Further testing is conducted to compare between AC and DC arcing. Preliminary results suggest a higher drop for the arcing voltage for DC voltage mode as compared to AC with increasing altitude. These preliminary outcomes could potentially help develop a diagnostic method for arcing failures of insulation systems in electrified aircraft applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".