Effects of Hydrothermal Aging on the Breakdown Voltage of Polyesterimide
Bibliographic record
Abstract
Polymers have always played a big role in the insulation of electrical machines and have been the focus of several studies when they are subjected to different stresses. The humidity and temperature are the main constraints that degrade the insulating performance of the materials when they act together causing hydrothermal aging of the polymers. This study deals with the influence of hydrothermal aging on the breakdown voltage of polyesterimide which is known as one of the most important factors which define the electrical insulation performance of the polymer. The breakdown test was executed under AC and DC voltage. A statistical analysis of breakdown data was carried out using the two-parameter Weibull distribution. According to the findings, the breakdown voltage changes with aging time. Its rise is due to a polymer crosslinking by thermal action, causing a reduction of the mean-free path of charge carriers decreasing their mobility. The decline is attributed to the material plasticization after water penetration into the polymer matrix at the beginning of aging, which is a physical effect. Eventually, the chemical effect reflects the hydrolysis reaction, which destroys the hydrogenous links of the polymer. The breakdown voltage under the DC ramp is higher than that obtained under the AC ramp. The DC breakdown voltage depends on the polarity of the applied electrical field. The polymer degradation is characterized by a change in color and the crumbling of samples.
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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 | 0.000 | 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".