Corrosion under insulation mitigation by passive multivariate thermography
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
Corrosion under insulation (CUI) is one of the major concerns of oil and petrochemical installations as damage evolves invisibly under insulation layers and usually revealed on the occurrence of leaking or more catastrophic failure. Methods to early detect CUI and its causes is an urgent necessity to assure safety and performance of insulated process piping. Oil and petrochemical plants are often considered explosive environments in which thermal excitation devices are forbidden. The present work aims then on the consolidation of a passive thermographic methodology to reliably detect moisture trapped under insulation layers that will cause corrosion. The proposed methodology focuses on the thermal behaviour of the piping structure during process variations and interactions with the external ambient. The partial least-squares analysis showed promising performance on separating different physical phenomena, creating cleaner images for defect detection and extending the applicability of infrared thermography to the lower levels of surface emissivity that characterises clad insulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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