Aging characterization of thermally aged transformer paper based on its reflectance
Bibliographic record
Abstract
In this contribution, a simple non-destructive characterization of aging degree of oil-paper insulation materials based on the reflectance is proposed. Samples of cellulose Kraft paper having different thicknesses, were thermally aged in a mineral insulating oil and a synthetic ester with a controlled aging history. The degree of polymerization of the non-aged and aged paper samples was measured according to ASTM D4243 to monitor the cellulose degradation. In addition, the samples were optically analyzed to assess changes in paper’s reflectance. The reflectance spectra of the thermally aged paper samples were statistically analyzed using linear, single variable, and multi-variable analyses by considering eight popular variables. This enables correlating the reflectance to the degree of polymerization and identifying a suitable regression model. Appropriate variable interaction has been performed among which two best-fit models with goodness of fit ≥ 0.9 have been identified. The estimation of the cellulose paper’s DP using the proposed models is reported. The experimental results show that the proposed approach can be used in characterizing aging degree of oil-paper insulation and has the potential to be implemented online as an effective monitoring technique.
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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) | 0.000 | 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 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".