Stator Inter-turn Fault Detection of Synchronous Machines Using Field Current Signature Analysis
Bibliographic record
Abstract
Our recent observations suggested that harmonics in the field current are very promising to detect stator inter-turn faults in synchronous machines. So far, an increase in some of the even harmonics in the field current has been reported to detect such faults. However no explanation has been provided for the cause of these harmonics. Moreover, the even harmonics can significantly increase with supply unbalance as well as time harmonics, which can lead to a serious confusion. Hence, in this study, an in-depth investigation was conducted to determine the origin of various harmonic components in the field current and their feasibility to detect stator faults. It was found that, owing to structural asymmetries of the field winding, some of these components clearly increased with stator inter-turn fault. The findings are helpful to detect faults involving few turns without ambiguity, in spite of with supply unbalance and time harmonics. Both simulation and experimental results are presented in this paper. The diagnosis results have also been verified using a rotor-mounted search-coil, which can also be used to detect even a one-turn stator fault very effectively
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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) | 0.001 | 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".