CKF-based PMSM local demagnetization fault diagnosis method
Bibliographic record
Abstract
A CKF-based PMSM local demagnetization fault diagnosis method is proposed for the problem that the on-board permanent magnet synchronous motor is prone to permanent magnet demagnetization faults due to the strong armature reaction under various complex operating conditions. A model of local demagnetization fault in permanent magnet synchronous motor combined with volumetric Kalman filter is constructed to realize the qualitative and quantitative description of fault characteristics. Then, by analyzing the air gap density of the motor when the local demagnetization fault occurs, comparing the Fourier spectrum of the air gap density before and after the fault, the harmonic components of different demagnetization fault degrees are summarized and summarized, and finally, by analyzing the magnetic chain signal and judging whether the local demagnetization occurs by the threshold value of the fault. The experimental results in Matlab/simulink simulation prove that the demagnetization fault diagnosis module can achieve high-quality diagnosis of local demagnetization faults by analyzing the DC components of the barrier features after volumetric Kalman filtering. The research results provide new ideas on the problem of fault-tolerant control of local demagnetization faults.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".