Development of an Automatic Recognition Model for Phase Resolved Partial Discharge Patterns in Hydro-Generators
Bibliographic record
Abstract
Whether in the industrial, medical or real-world domains, more and more data is being collected. The common particularity of all these application domains is that a great part of this data is mostly unlabeled. So, learning useful representations with minimal expert supervision is a major challenge in artificial intelligence in the coming years. Recently, there has been a particular interest in unsupervised learning methods based on the idea of autoencoding. The purpose of these methods is to learn a mapping representation from a high-dimensional to a lower-dimensional representation space from which the original observations can be reconstructed. The variational form of these autoencoders, called the Variational AutoEncoders (VAEs), is particularly successful in almost all application areas. This enthusiasm comes from the fact that VAEs allow to take advantage of the theoretical foundations of the Variational Bayesian methods and the learning capabilities of artificial neural networks. In this paper, a model based on the use of VAEs is proposed for the development of an automatic recognition and unsupervised feature extraction of Phase Resolved Partial Discharge (PRPD) results measured from hydro-generators. The first step of this model is the pre-processing phase where external noise were removed. The second step is the learning phase which is based on two concatenated VAEs. Preliminary results indicate that the two-dimensional (2D) reduced latent space configuration encodes the difference in the intensity of the PRPD patterns as well as in the phase shift. More improvement is needed to extract features related to the shape of the PRPD patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".