Use of an electronic nose to estimate paper insulation degradation
Bibliographic record
Abstract
The ultimate life of a transformer primarily depends on the insulation of its active part, i.e., mainly the oil and paper complex. At present, the tools used to directly determine the state of solid insulation require transformers to be detanked in order to take paper samples for laboratory analysis. Indirect methods (oil analysis) require a complex separation system and need accurate calibration of a detector. The electronic nose technology is now widely used in different industrial areas to detect the degradation of several convenience goods like wine, coffee and cheese. Based on metal oxide semiconductor sensors from different technologies, these instruments are designed to detect odours just like a human nose. They can be set to recognize different types of degradation processes, thus serving as an efficient diagnostic tool. This original method applied to the characterization of insulation paper aging should be easier to perform than ASTM D 4243, which required the oil to be removed from the paper, dissolving it, and then measuring the degree of polymerization (DPv) by viscosimetry. This paper presents the development of a method that uses an electronic nose to determine the degree of polymerization of paper samples processed through a head space system with an automatic sampler device without having to remove the oil. Aged laboratory samples were analyzed with the electronic nose and with the standard ASTM D 4243 test method to build a calibration curve. This curve was then used to determine the DPv value of different field paper samples and compared to ASTM D 4243 results. Until now, the method developed by IREQ is reproducible and less time-consuming than the standard method currently used by Hydro-Québec, but is unfortunately less precise (20% vs. 5%). More work is needed to optimize the calibration model to potentially improve the method's precision.
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 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 | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".