Wavelet Packet Decomposition Based Detection and Classification of Stator Winding Insulation Degradation for Electric Machines
Bibliographic record
Abstract
For electric motors, monitoring the condition of winding insulation becomes necessary to ensure a safe and reliable operation, which can help in the prevention of faults like short-circuit. In this paper, a novel insulation condition monitoring technique has been proposed, which employs wavelet packet decomposition (WPD) to analyze high frequency (HF) line current and extract indicators for monitoring the state of health (SOH). Compared with existing methods, the proposed technique can provide the SOH indicators of turn-to-turn (TT) and groundwall (GW) insulation simultaneously through the analysis of line current. Moreover, antiresonance oscillations in the HF line current are also explored to determine insulation SOH, which contributes to a unique capability in classifying types of degradation. The procedures for insulation degradation detection and classification are summarized in a flowchart for simple implementation. To validate the proposed method, extensive simulation and experimental tests have been conducted. The proposed method demonstrates robust performance, ability to detect even a small amount of degradation and possesses a unique capability to classify and quantify different types of degradation.
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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 | 0.001 | 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".