Multi-Sensor Fusion-Based Permanent Magnet Demagnetization Detection in Permanent Magnet Synchronous Machines
Bibliographic record
Abstract
When permanent magnet (PM) demagnetization in a PM synchronous machine (PMSM) occurs, the demagnetization characteristic information is usually coupled with other characteristics of the PMSM which may demonstrate similar features. Therefore, it is difficult to accurately detect the demagnetization by monitoring only one parameter. This paper explores the use of multi-sensor information, namely, acoustic noise and torque, for demagnetization detection through the information fusion technique. The acoustic noise and torque information are processed and analyzed using wavelet transforms for filtering and extracting features. Bayesian network-based multi-sensor information fusion is then proposed to detect the demagnetization ratio from the extracted features. The proposed approach is experimentally verified on a laboratory PMSM and compared with single-sensor dection methods.
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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) | 0.000 | 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".