On-Line Detection and Classification of Permanent Magnet Demagnetization using Observed Flux Linkage Signals
Bibliographic record
Abstract
Demagnetization in permanent magnet synchronous motor (PMSM), due to factors like high temperature or reverse magnetic field can lead to increased loss, torque fluctuations, and potential instability of the system. Detecting this demagnetization through online methods allows for the identification of permanent magnet (PM) weakening at the initial stage. In this paper, an online demagnetization detection technique is proposed by using a flux observer. The flux observer is constructed based on the voltage equations of PMSM in dq reference frame. It is used to estimate the fundamental and harmonic components of the flux linkages corresponding to uniform and partial demagnetizations. Fault indicators are established based on the estimated amplitude of special harmonics. Therefore, the proposed approach not only identifies PM demagnetization fault and its severity, but also distinguishes between different fault types. Additionally, the proposed method has less computational complexity and few variables, it is easy to implement without any extra hardware. The proposed approach is validated through simulations with the observer applied to estimate the flux linkages from a precise PMSM finite element model under no-load and loaded conditions with different demagnetization scenarios.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".