Evaluation of Thermally Aged and Gamma-Irradiated Nuclear Cables using Electrical Diagnostic Tests
Bibliographic record
Abstract
Electrical cables are essential within nuclear power plants (NPPs) to support power, control, and instrumentation systems. Given the importance of these systems in servicing NPPs, prevention and detection of electrical cable failure is a vital part of aging management programs. As such, nondestructive examination (NDE) techniques are commonly used to evaluate the degradation of electrical cables. Common offline electrical NDE techniques for condition monitoring (CM) may include lowfrequency dielectric spectroscopy (LFDS), time domain reflectometry (TDR), frequency domain reflectometry (FDR), and time domain dielectric spectroscopy (TDDS). However, there is no single NDE method to comprehensively evaluate cable condition and, in many cases, a combination of local and global tests is required. In this work, we evaluate the sequentially applied thermal and gamma radiation aging of electrical cable insulation using electrical diagnostic test methods to better understand damage detection in cables using a combination approach. Gamma radiation aging was performed on cross-linked polyethylene (XLPE) polymer cable insulation material commonly used in NPPs. Long “30-foot” cable mandrels of XLPE were irradiated at room temperature, using Co-60 gamma-rays, to intervals of 100 kGy each, for a combined total dose of 500 kGy. Post irradiation electrical testing was performed on these cables. Preliminary results of the electrical diagnostics indicate trends with increasing aging conditions in the XLPE cable insulation samples. The results of this study advance electrical-based diagnostic techniques for condition monitoring of electrical cables in NPPs, providing plant operators with more complete information to support repair, mitigation, or replacement decisions.
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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 | 0.001 | 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".