Real-Time Rotor Temperature Estimation in Permanent Magnet Synchronous Motor Using Incremental Deep Learning
Bibliographic record
Abstract
Rotor temperature estimation is crucial in permanent magnet synchronous motors (PMSMs) because it directly affects the performance, efficiency, and overall reliability of the motor. Accurately estimating the temperature in Permanent Magnet Synchronous Motors (PMSMs) is challenging due to their dynamic operation. However, placing sensors directly on the rotor is difficult because of its dynamic nature and mechanical constraints, which can also add to the costs involved in temperature monitoring. In this respect, it becomes imperative to develop effective methodologies that can cope with these dynamical changes for real-time estimation of the rotor temperature. This work puts forward a learning method that combines deep learning (DL) models with a novel incremental learning technique to achieve precise rotor temperature estimation in PMSMs. To mitigate the issues associated with catastrophic forgetting in incremental learning, it is proposed to preserve prior data streams in a buffer and selectively and periodically select the most informative ones. This technique enables DL models to learn from past experiences while mitigating the risk of losing valuable knowledge during the continuous learning process. Application of this learning scheme to real datasets from an experimental test bed demonstrates performance enhancement compared to models trained offline.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".