Bibliographic record
Abstract
The paper describes an innovative digital inspection methodology that combines 3D laser scanning, metrology and advanced non-destructive testing data that is merged in 3D space to provide a digital record of the condition and mechanical integrity of critical assets. This advanced inspection method supports condition-based maintenance programs and digital twin models to determine future equipment condition, work scope and inspection schedules, while maintaining a digital record throughout the equipment lifecycle. Testing of the methodology includes 3D scanning of drill platforms, baseline scanning of blowout preventers and sheaves, for quality purposes, and the use of augmented reality for viewing scans. Phased array testing has been conducted on sub-components such as slew ring bolting. Data are combined into digital reports that show 3D images of the equipment with precise dimensional data and identified inspection areas. Such reports can be combined with digital twin models to confirm integrity of the equipment for certificate of conformance and baseline data for future integrity comparisons as equipment ages. This innovative inspection methodology will set a new standard for how equipment data are captured, stored and represented. The process provides a range of benefits for OEMs, drilling contractors and operators alike, including digital quality programs to baseline new equipment condition and compare with design parameters, delivering condition and integrity assessments of critical equipment items in-situ or on deck, providing a consistent methodology for inspection and dimensional control of operational equipment items, and providing precise equipment data that can complement digital twin and real time monitoring programs.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".