Acoustic Noise-Based Uniform Permanent-Magnet Demagnetization Detection in SPMSM for High-Performance PMSM Drive
Bibliographic record
Abstract
This paper explores the idea of detecting uniform permanent-magnet (PM) demagnetization by using acoustic noises in order to develop a reliable PM synchronous machine (PMSM) controller. A flux-based acoustic noise model is proposed to demonstrate that demagnetization will induce acoustic noise containing abnormal frequency. This paper will also analyze online PM demagnetization detection by using a back propagation neural network (BPNN) with acoustic noise data. First, seven objective and psychoacoustic indicators are proposed to evaluate the acoustic noise of healthy and demagnetized PMSMs under different speed and load conditions. Next, a novel BPNN-based PM demagnetization detection method is proposed. In this method, the PM demagnetization is detected by means of comparing the measured acoustic signal of PMSM with an acoustic signal library of seven acoustical indicators. The proposed PM demagnetization detection approach is experimentally evaluated. Unlike other approaches, this is a noninvasive method and is independent of internal motor parameters. The aforementioned seven indicators can process nonlinear signals and are used to comprehensively reflect noise quality.
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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".