Integrated Induction Machine Eccentricity Modeling for Linear Amplifier Based Emulation
Bibliographic record
Abstract
Wound rotor induction motors are prone to eccentricity faults due to their structural characteristics. However, they are significantly less studied compared to cage induction motors. This paper presents a generalized analytical model for identifying and analyzing various eccentricity faults, including static, dynamic, and mixed eccentricities. It includes both axially uniform eccentricity and the less explored yet more complex and commonly occurring fault type known as inclined eccentricity. In addition, power hardware-in-the-loop (PHIL) emulation is employed as a testing method. PHIL enables replication of different eccentricity conditions without introducing the fault into the motor. A high-bandwidth linear amplifier is used for the emulation process to ensure precise replication of the stator current without generating additional harmonics. Experimental results from the PHIL emulator are compared against those obtained from a prototyped induction machine with eccentricity faults, confirming the accuracy of the proposed approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".