Assessing One-vs-All 1D-CNN Classifiers for Multi-Label Classification of Partial Discharge Waveforms in 3D-Printed Dielectric Samples with Different Void Sizes
Bibliographic record
Abstract
Effective insulation degradation diagnosis is essential for monitoring the reliability of any electrical system. One cause of degradation within insulation materials is the occurrence of partial discharge in voids. The severity of the degradation is related to the size of these voids inside the material. Hence, non-invasive classification of the void size could be important for cost-effective maintenance. However, multiple void sizes can exist concurrently within the insulation material which makes the problem a multi-label classification problem. In this paper, the performance of a collection of one-versus-all one-dimensional convolutional neural network (CNN) was investigated to classify different void sizes inside 3D-printed dielectric samples. Training of the CNN classification algorithm was done on single void-size samples and testing was done on single and multiple void-size samples. The CNN took a set of PD time-series waveforms as the input and investigation was carried out to assess the performance of such a system when multi-labeled signals were presented in the testing phase. In addition, the effect of the number of the classified classes on the performance of the proposed system was considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".