A Machine Learning based Approach for Detection and Quantification of Insulation Degradations in Machines' Stator Winding
Bibliographic record
Abstract
This paper presents a novel insulation condition monitoring approach for electric machines fed by voltage source inverters. The proposed method utilizes a machine learning (ML) algorithm to detect the degradation and quantify the overall state of health (SOH) of insulation by utilizing the features obtained by processing transient line current. To extract features from the transient line current, wavelet scattering is utilized. Here, capability to quantify the SOH is important, as it can be further utilized for remaining useful life determination and preventive maintenance. To thoroughly examine the performance, a dataset was constructed by emulating various degradation scenarios using a high frequency (HF) model of machine stator winding. The dataset includes transient line currents for different degradation scenarios and various noise levels. The proposed method demonstrates great performance on 640 unique test data, and it demonstrates the ability to detect a small amount of degradation even with highly noisy signals. This paper also presents a comprehensive framework for implementation of the proposed method, which addresses feature extraction, data generation, data augmentation, as well as training and validation of ML algorithm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".