Quantitative thermographic diagnostics of electric motors for reducing energy demand
Bibliographic record
Abstract
Excessive electrical demand leads to challenges in electrical supply and global climate change. Reductions of carbon dioxide emissions are necessary to mitigate the consequences of global climate change. Approximately thirty percent of the carbon dioxide comes from fossil fuels burned to generate electricity; therefore, the efficient use of electricity reduces carbon dioxide emissions. Estimates place electricity used by motors at 65%, so efficient use of motors is an opportunity to avoid carbon dioxide emissions. Traditionally motor efficiency is measured using a dynamometer and an electrical power meter; this is not practical in an industrial setting without process interruption. Therefore, a non-invasive technique for measuring in-situ motor operation is needed. Quantitative thermography is created and developed in this work as a non-invasive method to measure in-situ heat transfer coefficients and motor operation (load and efficiency). Inefficiencies, either based on motor load or equipment degradation, are identified by comparing the measured waste heat to the expected waste heat values. The method for quantitative thermography is validated in an electro-mechanical lab and applied in an industrial setting. The results of quantitative thermography in the laboratory indicate that accurate determination of efficiency of motors at loads over 60% and the identification of under loaded motors is possible. The results of quantitative thermography in industry show the accuracy of the efficiency is better than 2.5% based on the average variance of the heat transfer coefficient at 12.9%, which compares favourably to standard laboratory methods of measuring motor efficiency. The industrial trial shows the potential to reduce electrical demand of the motors, and their carbon footprint, by an average of 12%. Additionally, motor changes based on quantitative thermography have a positive net present value using a 20% required rate of return, medium risk, based on electrical savings and carbon dioxide avoidance. Extrapolating the results of this quantitative thermography industrial trial indicates that wide spread use of quantitative thermography has the potential to reduce world electrical demand by 8%, with a carbon dioxide equivalent avoidance of 470 Mt/y.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".