Detection of Eccentricity Faults in Three-Phase Reluctance Synchronous Motor
Bibliographic record
Abstract
Reluctance synchronous machines, particularly the newer ones with axially laminated anisotropic rotor, are being employed increasingly in many industrial applications as they offer energy efficient solutions along with low-cost rugged construction and zero speed regulation. High-performance requirements of the machine demand a high saliency ratio resulting in low air-gap length along the direct axis. Air-gap eccentricity diagnosis therefore becomes very significant. In this paper, the effects of different types of eccentricity faults in a commercially available reluctance synchronous motor (RSM) are first analyzed to identify the fault-specific frequency components in the line current spectrum. For validating the analyses, a modified-winding-function-based model and a finite-element-based model are built to simulate the motor under different eccentricity conditions. Experiments are then carried out on a three-phase RSM with a moderate to high level of eccentricity to confirm the theoretical prediction and simulation results. Finally, by applying residue-elimination technique, the effects of supply unbalance and internal asymmetry are minimized, and eccentricity is detected very reliably. These results will help noninvasive eccentricity fault detection in larger power salient-pole synchronous machines in the long run.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".