Measurement-based Dynamic Modelling in Levitated Active Magnetic Bearing Systems
Bibliographic record
Abstract
Commissioning Active Magnetic Bearings (AMB) requires a well-designed control system to reject external disturbances while supporting process loads. An accurate dynamic model is essential for predicting responses to various excitations and disturbances, aiding in controller design. Current models for AMB commissioning are either nonparametric estimations, which provide limited information, or finite element models, which may not capture all system dynamics. This paper presents a black-box method to develop a parametric model that captures all dynamics within an AMB system using measurement data. Frequency domain measurement data was used to construct a segmental linear model to simulate dynamic interactions. The controller, actuation, and sensor components were derived from SKF Magnetic Mechatronics design calculations to isolate the rotor from the plant model and reconstruct the closed-loop system model. The frequency response of the identified rotor model was compared to direct measurements, and the resulting system model was compared to direct measurements of the closed-loop response. Results show that the proposed segmental AMB system model predicts frequency response with 97.9% accuracy compared to direct measurements. This model will be used to design control methods to improve AMB system performance.
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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".