Condition Assessment and Failure Modes of Solid Dielectric Cables in Perspective
Bibliographic record
Abstract
Older solid dielectric cables are now approaching the end of their life and failing. Consequently there has been a sustained effort for the development of techniques to assess the remaining life of in-service cables for the purpose of establishing replacement criteria. Because of the generally accepted belief that the failure mode is due to the aging of the insulation, these techniques have mainly focused on measuring changes in the electrical properties of the insulation. It has been determined that there are other failure modes associated with loss of the protective function of the jacketing material leading to corrosion and breaks in the metallic shields and poor contact between semicon and metallic shield, however, causing arcing damage and eventual failure of the cable. This failure mode is more prevalent in cables operated in a harsh chemical environment. This paper presents the results of a research program aimed at developing a test protocol that is based on evaluating the condition of all the cable components, and not just the insulation. The tests were designed to evaluate the condition of jacket, extent of corrosion of the metallic shield, and degree of degradation of the cable insulation. The results are discussed in terms of correlations between the field measurement parameters and the condition of the cable, and prioritization criteria for cable replacement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".