Condition assessment of distribution PILC cables
Bibliographic record
Abstract
The condition assessment of paper insulated lead covered cables (PILC) cables is a crucial factor for many utilities and this paper is devoted to the evaluation of some new diagnostic techniques enabling users to effectively manage their PILC cable assets. We used electrical, metallurgical and chemical techniques to measure the electrical, chemical, dielectric and metallurgical properties of paper-impregnated insulation. The nondestructive electrical tests performed on three full-length field-aged PILC cables were: the isothermal relaxation current (IRC), the LIpATEST leakage current test and the return voltage method (RVM). Chemical tests were performed on small samples of paper tapes and oil taken from the same samples. They were: dielectric analysis, Fourier transform infra red (FTIR) spectroscopy and moisture content analysis. The electrical techniques ranked the cables consistently, that is one cable aged 23 years seemed to be more severely aged than the older (34 years) and younger (4 years) cables. This could possibly be explained by the acids detected in the oil of the 23 year old cable using FTIR spectroscopy. Although more data on more cables would be needed it already appears that the tested techniques could assess the condition of the insulation of PILC cables. Water ingress is often associated with cracks in the lead sheaths due to fatigue and creep failure. Metallurgical tests were performed on the lead sheaths of four PILC cables and they revealed that artificial cracks of different depths introduced on the surface of the lead sheaths were easily detected by the visual and fluorescent dye penetrant inspections. An eddy current inspection technique was successful in detecting the artificial surface flaws on the lead sheaths. The hardness of the older lead sheath, as measured by the Brinell test, tends to be lower than that of the lead sheath of the younger vintage cables.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".