Analysis of Clarke Vector Hodograph Shape for Diagnostics of Inter-Turn Fault in Stators of Induction Electric Motors
Bibliographic record
Abstract
In this article, a novel approach is presented for assessing the quantity of compromised stator windings utilizing the Clarke vector hodograph (later referred to as “hodograph”). The fault is understood in the context of inter-turn short-circuits of stator windings in an induction motor. Experimental investigation on a dedicated test rig reveals that with undamaged windings, the hodograph manifests itself as a circle; otherwise, it assumes an elliptical form. The angle of inclination of the ellipse, as well as the degree of flattening, indicates the presence of the fault that can be quantified. To detect faults related to stator defects, an algorithm is proposed that allows for parameterizing the features and investigating their monotonicity. Furthermore, an analysis of feature distribution per fault case is performed. These investigations led to the construction of a statistical model in 2-D feature space that allows for performing model-based diagnostics and the quantification of the severity of the fault.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".