Identification of spectral components in the line current of eccentric salient pole machines using a binomial series‐based inverse air‐gap function
Bibliographic record
Abstract
Current literature does not provide any generalised method for specific permeance – magneto‐motive force‐based approach to predict all possible harmonic components in the air‐gap flux and line current of an eccentric salient‐pole machine. This study provides an elegant solution to express specific permeance of an eccentric reluctance synchronous machine as a summation of constant coefficient co‐sinusoidal terms. Binomial series expansion has been used to achieve this. The analysis has been validated by matching the presence of the predicted harmonic components in the stator line current by coupled magnetic circuit simulation based on modified winding function approach and experimental results. It is also shown that the effect of sensor errors, machine asymmetry, supply harmonics etc. can be minimised by residual estimation to vastly improve detection sensitivity under all load conditions. Thereafter, a procedure to identify fault type and severity has been presented.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".