Spectral Graph Convolutional Networks for Rotor Temperature Estimation in Permanent Magnet Synchronous Machines
Bibliographic record
Abstract
Estimating rotor temperature is a challenging problem, yet an indispensable aspect of modern permanent magnet synchronous machines (PMSMs). This is critically important for health monitoring in PMSMs operating under various conditions. This paper presents a data-driven estimation framework by leveraging spectral graph convolutional networks (GCN) due to their low computational overhead and improved real-time performance. In particular, we construct several datasets using an experimental setup to evaluate the estimation performance under diverse operating conditions. Each dataset is initially processed using a novel graph-based correlation-aware mechanism to construct a graph with strongly connected nodes. This graph is then fed into the GCN module for regression. An enhanced GCN model using Chebyshev convolution is proposed to capture the temporal and overall data patterns within each dataset. Compared with a regular GCN, results show improved performance and superiority of the enhanced GCN for rotor temperature estimation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".