Energy Conservation Model for Electromechanical Transient Characteristics of Electromagnetic Actuators
Bibliographic record
Abstract
Fault diagnosis and bounce reduction of electromagnetic actuators require one to obtain the electromechanical transient characteristics (ETCs) in real-time. ETCs can be measured by sensors, but due to their large size and intrusiveness, the approach has its limitations. Acquiring ETCs through finite element method (FEM) could be an alternative, except that FEM is known to be time consuming. In contrast, real-time measurement of coil current is not subjected to these restrictions, yet, it has not been properly utilized for calculating ETCs. In this regard, this paper analyzes the source of the armature kinetic energy, and thus proposes a model based on energy conservation (ECM), describing the relationship between the mechanical and electrical characteristics. Based on the model, ETCs can be calculated from coil current, moving part mass, and the static counterforce. To exemplify the effectiveness of the procedure, an electromagnetic contactor is considered, and the results obtained using the ECM are found fairly consistent with that obtained using sensors. The merit of the proposed ECM lies in its non-intrusive nature and its ability to circumvent the time-consuming FEM in calculating ETCs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".