A Novel Algorithm for Estimating Refurbished Three-Phase Induction Motors Efficiency Using Only No-Load Tests
Bibliographic record
Abstract
Induction motors fail due to many reasons, and many are rewound two or more times during their lifetimes. It is generally assumed that a rewound motor is not as efficient as the original motor. Precise estimation of efficiency of a refurbished motor or any existing motor is crucial in industries for energy savings, auditing, and management. Full-load and partial-load efficiency can be measured by using the dynamometer. This paper presents a novel technique for estimating refurbished induction motors' full-load and partial-load efficiencies from only no-load tests. The technique can be applied in any electric motor workshop and eliminates the need for the dynamometer procedure. It also eliminates the need for the locked-rotor test. Experimental and field results of testing eight induction motors are presented, and the degree of accuracy is shown by comparing the estimated efficiencies against the measured values. To provide the necessary credits to the proposed technique, an error analysis is conducted to investigate the level of uncertainty through testing three induction motors, and the results of uncertainty of the direct measurements and no-load measurements using the proposed technique are presented.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".