Classification Algorithms Comparison for Interturn Short-Circuit Recognition in Induction Machines Using Best-Fit 3-D-Ellipse Method
Bibliographic record
Abstract
Induction machines are omnipresent in industry because of their sturdiness and their ease of implementation. Nevertheless, these electrical motors still concede failures [e.g., interturn short circuit (ITSC) and broken rotor bar], which may lead to unplanned shutdowns. Consequently, manufacturing industries invest significant resources to avoid them with maintenance. Some studies have been achieved in this area of research, but any of the optimal solution (detecting, localizing, and estimating the degree of severity of failures) has been developed. Thus, in this paper, we propose to perform a comparison of performance and robustness between different classification algorithms, which can detect, approximate (severity of the failure), and localize (which phase) the ITSC in the stator phase(s) of the three-phase induction machine. To the best of our knowledge, it is the first time that such an evaluation has been suggested by using automated classification into predefined categories for ITSC in the stator phase(s) detection (recognition). This paper aims at providing an understanding vision of the recognition of failures that may occur, in order to develop future optimal solutions, which will be deployed in industry environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".