Evaluating OLTC condition based on feature extraction from vibro-acoustic signals
Bibliographic record
Abstract
The On Load Tap Changer (OLTC) faults account for about 30% of the power transformer failures. Transformers are one of the most critical assets in the electric power network. Therefore, monitoring this component continuously and detecting any incipient faults is essential to prevent power transformer outages. In this paper, vibro-acoustic signal analysis has been adopted to evaluate the OLTC condition. Vibration signals obtained from the in-service on OLTCs are analysed using statistical parameters in two steps. The first step evaluates the signal envelope to identify changes over the operating period. In this regard, each vibration signal envelope is compared with a reference envelope, and the degree of similarity is estimated by Pearson’s correlation coefficient, mean similarity, and peak similarity. The similarity values are later used to identify changes over the time elapsed to derive general information about the envelopes. In the second step, vibration signal envelopes are subdivided into six parts based on the peak positions. Subsequently, the statistical parameters are computed for each part, and the best fit ones are identified based on regression coefficients. The proposed approach can be seen as a useful tool allowing extracting features with potential to detect OLTC faults.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".