Efficient Hairpin Winding Fault Detection Using Impedance Measurements
Bibliographic record
Abstract
This study investigates various hairpin winding faults in electric motors using impedance measurements. Both high-frequency and low-frequency impedances are measured to characterize winding fault conditions. This study proposes various techniques to extract distinctive feature patterns that are associated with fault conditions. These include open circuit faults that show significant discrimination within the low-frequency range, and welding mismatch faults that are distinguished by a proposed similarity indicator. Insulation faults - faults that are related to epoxy - are found to be more difficult to diagnose using simple statistical metrics, so this study proposes a machine learning classification model using a support vector machine (SVM). The results show that the SVM model achieves a high accuracy using a small number of training samples. The methods discussed provide cost-efficient solutions to effectively detect welding and insulation faults, ensuring the product quality of hairpin windings.
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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 | 0.000 | 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 | 0.000 |
| 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 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".