Detection and severity estimation of static and dynamic eccentricity in induction motors using finite element analysis
Bibliographic record
Abstract
This paper presents a diagnostic scheme to detect the presence of static eccentricity (SE) and dynamic eccentricity (DE) faults in induction motors (IMs) by finite element analysis (FEA). Novel methods have been developed to detect these two faults and distinguish them so that suitable safety measures can be taken to mitigate these faults. Power spectral density (PSD) analysis on the stator currents provided the identification index to determine the presence of the faults and to determine its severity level. This scheme is simple as it uses commercially available finite element package - Maxwell, and also does not require complicated calculations. The procedure will be useful in identifying the level of fault with reasonable accuracy at higher loads with the simulation models; based on experimentation on the real motor only under healthy condition. Simulations have been performed with varying load conditions and different levels of eccentricity and these results have been compared with the experiments to verify the effectiveness of this diagnostic scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".