Comparison of an Automatic Classification of Partial Dischage Patterns for Large Hydrogenerator
Bibliographic record
Abstract
More and more scientific disciplines are using deep learning techniques for the automatic classification of massive high dimensionality unlabeled data. Among these disciplines, the classification of partial discharge (PD) patterns is one that represents major challenges in the field of hydrogenerator diagnosis. This paper proposes a method of comparison of five classification topology based on a single convolutional variational autoencoder (CVAE) and ten classifiers. The comparison is based on five cases exploiting all the same database, but using five different feature extraction rules to create the input vectors of the neural networks. These feature extraction rules are based on the expert judgement and are automatically computed in the pre-processing stage. Analysis of the output of all classifiers for each topology suggests that the accuracy level of the classification can be significantly improved by refining the feature extraction rules. Moreover, the visualization of the 2D latent space from the CVAE also suggests that the accuracy level can be even further improved if the whole dataset is considered instead of a smaller reference dataset randomly selected. Results raise many questions about the performance of feature extraction rules and the possibilities to better handling classification of large databases such as the one of PD measurement files used for hydrogenerator diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".