Estimated Environmental Impact of AC Electric Machine Reconditioning and Rewinding
Bibliographic record
Abstract
Electric machines are essential to activities that serve as cornerstones to our economy, such as the petroleum, chemical, mining, steel, pulp and paper, aggregate and cement and power generation industries. Electric motors serve as the prime movers for applications such as generators, pumps, fans, compressors, and extruders. Motors for these applications can range immensely in size and capacity from fractional to 250 HP, in low-voltage NEMA frame motors to many thousands of horsepower in medium-and high-voltage induction and synchronous motors manufactured in Above-NEMA frame sizes.In service motors and generators require routine maintenance and reconditioning every three to seven years depending on the operating environment to extend their useful service life. When motors fail prematurely for either mechanical or electrical reasons, they must either be replaced, or undergo extensive repairs, generally through reconditioning and/or rewinding. Repair of industrial motors can result in meaningful environmental benefits via two main impact pathways: recovery of active material (primarily steel, copper, and aluminum), thereby reducing the need for mining and manufacturing of virgin materials; and efficiency gains in cases where motors are rewound with additional copper content beyond OEM (Original Equipment Manufacturer) specifications along with improved thermal transfer characteristics. In this study, the authors conducted an analysis of the environmental impact associated with 18 365 motor repairs, including emissions of carbon dioxide (measured as metric tons of CO2) potentially avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".