Towards the Development of a Fitness for Service Tool for the Inspection of Corrosion under Insulation
Bibliographic record
Abstract
Abstract Corrosion under insulation (CUI) is a major problem for industry. Extensive removal and reinstatement of insulation for inspection is prohibitively costly. A non-destructive testing (NDT) tool which can characterise areas of CUI damage in insulated piping and pipelines with minimal removal of insulation is required. Sufficient data about the CUI is required to allow Fitness for Service to be assessed. Guided wave testing (LRUT) is a long range ultrasonic technique that was developed in the mid 1990’s to provide the solution to CUI inspection of piping and pipelines. The technology, understanding and application of LRUT has been continuously developed over the last 15 years, but is still a screening technique which is currently unable to provide dimensions of CUI. Indications reported by LRUT must thus be followed up using a second NDT technique to confirm and quantify CUI damage. A project sponsored by the Petroleum Institute, Abu Dhabi, has been established to improve on commercially available LRUT and develop a Fitness for Service tool to allow integrity management decisions to be made from LRUT inspections. In the first phase of the project, an independent evaluation of the performance of LRUT was performed, in terms of probability of detection (POD) for the system and operators/analysts. A purpose built test loop was constructed to be representative of real-world conditions, including metal loss defects simulating CUI. Future phases will (i) evaluate and develop better understanding of key variables and factors affecting the performance of LRUT, aiming at developing best practice guidance, and (ii) develop a Fitness for Service tool based directly on LRUT data. The results of the first phase are presented in this paper, with a number of interesting observations and lessons learnt that can aid in improving the outcomes from LRUT for CUI and for piping and pipelines in general.
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 | 0.000 |
| 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 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".