Modeling and Minimization of Speed Ripple of a Faulty Induction Motor With Broken Rotor Bars
Bibliographic record
Abstract
This paper presents a technique of modeling and minimization of speed ripples of a vector-controlled faulty induction motor (IM) with broken rotor bars. First, the performance of the faulty IM is investigated in terms of speed ripples under the open-loop condition. Then, a new IM model is developed, incorporating the speed ripples. Consequently, a new neuro-fuzzy controller (NFC) is proposed to tolerate the fault effect under an indirect field-oriented control scheme. The proposed NFC can compensate the motor rotation imperfections by minimizing the supply frequency-related speed ripples instead of directly working on the low-frequency speed ripples exhibited by a faulty IM. A novel self-tuning algorithm is proposed to adjust the weight of the developed NFC, and its training convergence is investigated by simulation. The effectiveness of the developed NFC is verified by both simulation and experimental tests.
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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 | 0.001 |
| 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".