Detection of Stator Faults in Induction Machines Using Residual Saturation Harmonics
Why this work is in the frame
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Bibliographic record
Abstract
Stator fault is one of the most commonly occurring faults in ac machines. Recent estimates suggest that 30%-40% of all reported induction machine faults are stator fault related. Going by the number of occurrences, the position of stator faults is only second to bearing-related faults. Third harmonic line currents, which are caused by the interaction of a reverse-rotating field and saturation-related permeance variation, the third harmonic component in the line voltage, and the speed ripple consequent to the reverse-rotating field, have been reported to increase under stator fault conditions. Since the reverse-rotating field is produced by voltage unbalance as well as stator faults, the measurement of third harmonic in the line current may not be a reliable estimator of stator faults, particularly at an incipient stage. The third and the other triplen-related harmonics are however found to be a very decisive indicator of the fault if measured in the machine terminal voltages just after switch-off. The fault-detection technique is independent of machine parameters and supply unbalances. Simulation and experimental results with very few shorted turns show that not only the presence of stator fault but also the phase in which the fault has occurred can be detected reliably. However, because of the nature of detection, it should strictly be called an offline method that can only validate existing online schemes, unless used for motors operating continuously in transient modes or form-wound machines
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it