Feature Extraction of Partial Discharges During Multiple Simultaneous Defects in Low-Voltage Electric Machines
Bibliographic record
Abstract
Partial discharge (PD) defects are initiated in the electrical machines over different life stages. The intensity of PD defects increases continuously with time, which may lead to insulation failure. In some cases, multiple PD defects may occur concurrently leading to a faster equipment failure.This article presents a methodology for the separation of multiple PD defects in low-voltage motors. The characteristics of the recorded PD signals have been investigated to estimate the severity of these defects. The cumulative energy (CE) function in the time domain has been calculated from the PD signals and its significant features, which include amplitude, range, skewness, kurtosis, autocorrelation functions, cross correlation functions, and width parameter of CE signals, have been compared for the separation of various defects. Therefore, 6-D feature space consisting of peak value, dispersion, symmetry, sharpness, similarity, and shape features of CE functions has been produced for the separation of multiple defects in the motors. Finally, the K-mean clustering classification algorithm has been adopted using significant features of CE functions to discover their clusters in the feature space. The proposed algorithm has been validated based on the adjusted Rand index (ARI) function. Thus, it has been observed that the proposed procedure is effective for the separation of mixed PD signals from multiple defects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".