Multi-Sensor Fusion Based Permanet Magnet Demagnetization Detection in Permanet Magnet Synchrounous Machines
Bibliographic record
Abstract
Most of the demagnetization detection techniques are based on single sensor diagnosis such as analysis of stator current, acoustic noise, or torque. However, single sensor demagnetization detection has inherent uncertainties due to fault models and motor operating environments. Hence, multi-sensor information fusion is an effective way to solve such uncertainties and improve demagnetization detection accuracy and improve motor control stability. This paper explores the use of acoustic noise and torque ripple for on-line PM demagnetization detection by using the multisensor information fusion method. Both noise and torque signals are first analyzed and processed by wavelet transforms for de-noising and feature value extraction. Moreover, multi-sensor information fusion is applied to estimate the demagnetization ratio based on the support vector machine (SVM) training set. The proposed demagnetization detection approach is experimentally verified on a laboratory PMSM and compared with single-sensor detection method.
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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 | 0.000 | 0.001 |
| 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 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".