Towards winding deformation assessment from vibration signals using an optical sensor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 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 | 0.001 |
| 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 it