Towards winding deformation assessment from vibration signals using an optical sensor
Bibliographic record
Abstract
Abstract In the era of Industry 4.0, there is a growing emphasis on the digitization of electrical networks. Over recent decades, the integration of interconnected digital technologies, including sensors and communication systems, within electrical substations has emerged as a significant driver. Consequently, there is an increasing need for precise online monitoring of critical assets such as power transformers to enhance grid reliability. This study utilizes an optical‐based Fiber Bragg Grating (FBG) sensor to capture vibration signals from a custom‐designed single‐phase transformer model, specifically developed for experimental purposes. This model offers a unique advantage with its ability to interchangeably simulate healthy and distorted winding sections without causing damage. Using a high current source, the laboratory model was subjected to three different current levels across six distinct configurations to monitor winding displacements. The results from this investigation highlight the FBG sensor's capability to accurately distinguish between healthy and distorted winding sections. Furthermore, this feasibility study represents a significant step forward in the online mechanical assessment of transformer windings, moving away from traditional methods that require transformers to be taken out of service for inspection. This innovative approach shows considerable potential for implementing effective real‐time monitoring of winding deformation in power transformers.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".