Real-Time Modeling of Static, Dynamic and Mixed Eccentricity in Permanent Magnet Synchronous Machines
Bibliographic record
Abstract
Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article proposes making a circuit-type model of a permanent magnet machine with an easily configurable eccentricity for simulations and real-time analysis of signals under different operating conditions. The basis for the construction of the circuit model will be the simulation of the PMSM with 49 different coordinates of the rotor center, using the finite element analysis (FEA). The presence of eccentricity causes a variation in the inductances, the no-load flux and the expansion torque depending on the position of the rotor. The model proposes the use of bilinear interpolation (BI) to estimate the inductance matrix, the no-load flux vector captured by the stator winding and the cogging torque due to the presence of the magnets in the rotor, all of them for each rotor position. The validation is done by comparing the precision in the results of the machine’s self-inductances, the torque and the voltage waveform at the PMSM terminals and the static torque of the PMSM. The circuit model results are validated in two ways: (1) through experimental simulation, comparing the same results obtained using FEA and (2) through practical experimentation, producing a dynamic eccentricity in the machine of 0.3 mm. The results show that the proposed model is capable of accurately reproducing the behavior of the PMSM against eccentricity faults and presents computational time savings close to 99% compared to the response time obtained using FEA. This rapid PMSM model, parameterizable according to the degree of eccentricity, is the basis for the real-time simulation of the main machine waveforms, such as voltage, current and torque.
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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 | 0.001 | 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".