Radio frequency (RF) technique for field inspection of porcelain insulators
Bibliographic record
Abstract
North American utilities are increasingly concerned with their ageing ceramic insulator assets as they are either fast approaching their expected end of life or have already exceeded them. Defects like broken, cracked and punctured discs are some of the most adverse problems that the utilities encounter. These defects give rise to the initiation of partial discharge (PD) activities within the samples which has a detrimental effect on the insulator life. Hence it is important for the utilities to identify such defective samples as early as possible so that appropriate replacement strategies can be devised. Currently used PD techniques are off-line and are not suitable for detecting defective insulators in the field without interrupting the power supply. In this work, experiments are performed; simulating the actual field environment in an effort to develop a non-contact radio frequency (RF) based condition monitoring system for defective ceramic insulators. RF signatures captured in the field due to PD activities from two different defects are post processed; which involves noise removal and other signal processing techniques to extract appropriate wavelet packet based features. These features are then used to train and test artificial neural network (ANN) classifier. For the tests conducted, high recognition rates above 90% have been achieved.
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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".