Stability and Repeatability Analysis of a Phase-Modulated Optical Fibre Sensor for Transformer Oil Ageing Detection
Bibliographic record
Abstract
Transformer oil ageing alters key physicochemical properties, notably the refractive index (RI), due to physical, particulate, and chemical changes. As a result, refractometric fibre-optic sensors have gained attention for enabling real-time monitoring and overcoming the limitations of traditional offline diagnostics. This study explores the use of a Fabry–Pérot phase-modulated fibre optic sensor (FISO FRI RI Sensor) for in-situ ageing detection in four industrial transformer oils: natural ester, synthetic ester, Nytro Bio 300X (vegetable-based), and Polaris GX (mineral-based). The oils were thermally aged under controlled conditions following degassing and drying. The sensor performance was evaluated using key metrics, including repeatability, thermal response, settling time, and linearity. Results show high repeatability (with standard deviations below 7 × 10−5 RIU and repeatability coefficients under 2 × 10−4 RIU), stable thermal response (~0.0004 RIU/°C), and strong thermal linearity (R2 > 0.99) across all samples. Natural ester and Nytro Bio 300X exhibited the most stable and consistent sensor responses, while synthetic ester and mineral oils showed greater variability due to temperature-induced RI shifts. These findings demonstrate the reliability and precision of this Fabry–Pérot phase-modulated sensor for online transformer oil condition monitoring, with strong potential for integration into smart grid diagnostics.
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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 | 0.003 |
| 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 | 0.001 | 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".