Vold-Kalman Filtering Order Tracking Based Rotor Flux Linkage Monitoring in PMSM
Bibliographic record
Abstract
Monitoring permanent magnet (PM) flux linkage is important to maintain a stable permanent magnet synchronous motor (PMSM) operation. In this paper, V old-Kalman filtering order tracking (VKF-OT) and dynamic Bayesian network (DBN) are used to investigate the application of torque ripple in real-time PM flux monitoring. Firstly, a torque ripple model of PMSM considering electromagnetic noise is proposed, and the torque variation is studied. In this model, the torque is analyzed and processed by wavelet transform to eliminate the effects of the electromagnetic disturbances. Secondly, VKF-OT is introduced to track the order of torque ripple of PMSM running in unsteady state. Therefore, torque ripple characteristics can be used as a feature to reflect changes in PM flux linkage. Thirdly, this method is feasible for PMSM by applying DBN to the training data to estimate the flux linkage during motor operation. The proposed flux monitoring method is validated on a laboratory PMSM. The results demonstrate that this method can monitor the flux variation over a wide speed range at different load levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".