An Accelerated Testing Framework for Assessing the DC Erosion Resistance of Silicone Rubber Composites
Bibliographic record
Abstract
This paper studies the use of an accelerated and controllable testing framework to asses the DC erosion resistance of silicone rubber composites used for high voltage outdoor insulation applications. The framework examines the use of repeatable testing methods that generate controlled dry-band arcing under wet and dry conditions using a modified DC setup of the Comparative Tracking Index test and the AC dry-arc resistance test respectively. The composites are tested under the DC Inclined Plane Tracking-Erosion test with the outcomes used as reference for the composites' DC erosion resistance. With respect to the DC Inclined Plane test, the results indicate similar erosion resistance outcomes to the accelerated tests with a more distinct variance in the outcomes under the DC Comparative Tracking Index test. Moreover, a distinct pattern for the leakage current waveforms are observed amongst the composites under the DC Comparative Tracking Index test which correlates with the DC erosion resistance outcomes. The outcomes of the accelerated tests are repeatable, controllable and thus provide low uncertainty in the measurements for assessing the erosion resistance of silicone rubber. These observations could be utilized within the proposed accelerated framework to supplement the DC Inclined Plane Tracking-Erosion test in assessing the DC erosion resistance of silicone rubber composites for outdoor insulation 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 | 0.002 | 0.002 |
| 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 | 0.000 |
| 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".