Modified Efficiency Estimation Tool for Three-Phase Induction Motors
Bibliographic record
Abstract
This article presents a modified version of novel algorithms the authors previously proposed to help North America electric motor service centers to evaluate their repaired, rewound, or any existing motors for efficiency. Due to the dependency of the algorithms on a large 60 Hz induction motor database to produce reliable and accurate results, a software was needed and developed to incorporate both the algorithms and the data to create a useful single affordable tool for the North America electric motor repair industry. To eliminate the need for the software, and allow an open-source implementation, the direct dependency of the efficiency algorithms on the motor test data are eliminated in this paper. This allows the two techniques presented here to be used directly by engineers in the motor repair industry in North America. To achieve this goal, modifications to the two original algorithms are required and proposed in this paper. The modifications include the implementation of a new stray-load loss formula and the IEEE Std 112 hot temperature formula. The modified algorithms are validated by testing 28 new and aged, small- and medium-sized induction motors in the range of 1-500 hp. The modified techniques demonstrate a high level of accuracy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".