Expectation-Maximization Particle-Filter- and Kalman-Filter-Based Permanent Magnet Temperature Estimation for PMSM Condition Monitoring Using High-Frequency Signal Injection
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Bibliographic record
Abstract
In permanent magnet synchronous machine, high-frequency (HF) signal injection has been extensively investigated for permanent magnet temperature (PMT) estimation, in which PMT is estimated from the temperature-dependent HF resistance. Existing studies require prior knowledge on the HF resistance and neglect the fact that PMT is temporally correlated. This paper proposes a state-space model for PMT estimation, in which PMT is modeled with a piecewise linear equation to explore the temporal correlation. The state-space model is nonlinear due to unknown model parameters, which is required to be known in existing studies. This paper proposes to use expectation maximization particle filter (EM-PF) for simultaneous PMT and model parameter estimation. After EM-PF estimation, the state-space model becomes linear, so Kalman filter is employed for online PMT estimation. The proposed EM-PF along with a Kalman-filter-based approach can explore the temporal correlation among PMTs to improve the estimation performance, which can be hardly achieved in existing studies regarding PMT as a time-independent parameter. It should be noted that EM-PF is for initial PMT and model parameter estimation, while Kalman filter is for online PMT estimation ensuring computation efficiency and real-time capability. Our approach is validated with both numerical and experimental investigations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it