Gradient Boosting Decision Tree for Rotor Temperature Estimation in Permanent Magnet Synchronous Motors
Bibliographic record
Abstract
The increasing power density of permanent magnet synchronous motors has led to a severe motor heating problem that demands precise rotor temperature information to avoid demagnetization. Traditional temperature estimation techniques rely on thermal models that require specialized knowledge in motor design, thermodynamics, and material science. However, thermal parameters are often hard to obtain in real applications and contain significant mismatches when the working environment of motors varies. To overcome this challenge, this letter proposes an ensemble data-driven approach using the gradient boosting decision tree (GBDT) to estimate the temperature of the permanent magnet. The proposed scheme uses the temperature data at the stator tooth, winding, and yoke to predict the rotor temperature. The GBDT technique offers advantages in terms of accuracy and versatility due to its strong capability in handling complex data in various motor operating conditions, making it well-suited for industrial applications. The experimental results of a high-power machine validate the greatly improved accuracy in rotor temperature estimation over other approaches.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".