Detection of Stator Faults in Induction Machines using Residual Saturation Harmonics
Bibliographic record
Abstract
Stator fault is one of the most commonly occurring fault in AC machines. Recent estimates suggests 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, caused by the interaction of reverse rotating field and saturation related permeance variation, third harmonic component in the line voltage, and the speed ripple consequent to reverse rotating field, have been reported to increase under stator fault conditions. Since reverse rotating field produced by voltage unbalance cannot be distinguished from those produced from stator faults, the measurement of third harmonic in 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 as well as 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
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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 | 0.001 | 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 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".