Partial discharge pulse pattern recognition using an inductive inference algorithm
Bibliographic record
Abstract
This paper presents a novel approach in the area of time dependent partial discharge (PD) pulse pattern recognition, to applications based on the inductive learning (decision tree) approach. Different attributes based on pulse shape analysis are used as representative feature vectors that can accurately capture the unique and salient characteristics of the PD pulse shape. In the training phase, a decision tree is developed to relate the pulse shape with the cavity size by using inductive machine learning. The C4.5 machine learning algorithm is deployed to realize the tree using the training data, since it has the capability of inferring the rules and to produce the tree in terms of continuous features. During testing, the cavity size is recognized by means of the rules extracted from the decision tree. The dependency between the features and the classes are examined using the mutual information approach. The proposed algorithm possesses the inherent advantage of explaining the result via the self-created rule base as demonstrated by the results obtained. Those self-created rules can be employed as the basis for applying a fuzzy expert system for the classification of void sizes in an easily interpreted fashion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".