Comparison of results for eccentric cage induction motor using Finite Element method and Modified Winding Function Approach
Bibliographic record
Abstract
Air-gap eccentricity is a fault that mainly affects large induction motors. In the worst case an eccentricity fault can result in a stator rotor rub thereby causing severe damage to the motor. As a result eccentricity fault detection has gained considerable significance. Of all detection schemes for this fault, Motor Current Signature Analysis (MCSA) is the most widely used technique. This scheme relies on identifying fault specific frequency component in the line current spectrum of the motor to identify the type of fault. The severity of the fault can be estimated by monitoring the magnitude of the characteristic frequency component. To characterize the fault severity an accurate model of induction motor has to be developed. Two models - Finite Element(FE) based model and Modified Winding Function Approach (MWFA) based model are used in this paper to estimate the magnitude of fault specific frequency components for different eccentricities at full load condition. The results obtained from the two methods are compared to find the suitability of MWFA based model in characterizing the fault severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".