Modeling and Minimization of Speed Ripple of a Faulty Induction Motor with Broken Rotor Bars
Bibliographic record
Abstract
This paper presents the modeling and minimization technique for speed ripple of a vector controlled faulty induction motor (FIM) with broken rotor bars. First, the performance of the FIM is investigated in terms of speed ripple under the open-loop condition. Then, a new mechanical model of induction motor is developed incorporating the speed ripple. Based on this model a new NFC is proposed to tolerant the effect of the fault under an indirect field oriented control scheme. The proposed NFC compensates the faulty condition by minimizing the supply frequency related speed ripples instead of directly working on the low frequency signature speed ripples which a FIM exhibits. Based on the knowledge of motor control and intelligent algorithms an unsupervised self-tuning method is developed to adjust weights of the proposed NFC. The convergence of the weights is also discussed and investigated in simulation. The complete drive is experimentally implemented using a digital signal processor board DS-1104 for a laboratory 250 W faulty IM. The effectiveness of the proposed NFC is tested both in simulation and experiment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".