New Non-Destructive Condition Monitoring Techniques for On-Site Assessment of Low-Voltage Cables
Bibliographic record
Abstract
The number of techniques available for on-site monitoring of low-voltage cables at nuclear plants is limited because of the requirement from station personnel to use non-destructive and non-intrusive techniques. This paper summarizes the results obtained to date using two new non-destructive methods. The first technique is based on the indentation of the cable insulation or cable jacket material and the study of post-indentation parameters to characterize the visco-elastic properties of the material tested. The novelty of the technique consists of measuring the time taken by the polymeric material to recover a set portion of the initial deformation and using this duration as a material degradation indicator. The technique can be used locally on the insulation of hook-up cables, on the insulation at the termination of jacketed cables, or directly on cable jackets. The second technique is based on the measurement of electrical dissipation factors (or tan delta) in the insulating material. A broadband frequency tan delta analyser was used to measure electrical dissipation factors at various frequencies, and identify the frequency ranges showing increased sensitivity to cable degradation. Specific electrodes and measurement methods were developed for practical on-site condition monitoring work. The measurement of electrical dissipation factors can be used to assess the local degradation of cable insulation in hook-up cables and global degradation of multi-pair conductor cables. When used on multi-pair conductor cables, the technique presents the advantage of providing a global indication of the cable condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".