Digital Twin for Industrial Asset Management: A Case Study for Pipeline Maintenance
Bibliographic record
Abstract
Industrial asset management (IAM) is crucial for the sustainability and efficiency of industries that depend on significant infrastructure, especially pipelines. Pipelines are vital assets in the global energy supply chain, transporting oil, natural gas, and chemical products, and their maintenance is essential to prevent severe environmental and economic consequences. However, current methods such as manual visual inspections are limited by their invasiveness, the requirement for periodic shutdowns, and a lack of real-time accuracy. These limitations present substantial challenges to effective IAM. This paper introduces an innovative digital twin ecosystem integrated with information and communication technology to enhance IAM for pipelines. This ecosystem creates a dynamic, interactive digital twin that accurately reflects the physical state of pipelines, bolstered by real-time data transmission from sensors. The ecosystem comprises physical pipelines equipped with sensors, a comprehensive data knowledge library that records and updates damage information, a virtual pipeline twin, and an interactive platform that facilitates detailed visualization and interaction. Through a detailed case study of pipeline damage detection on cracks and corrosion using visual imaging techniques, this paper demonstrates enhanced results and visualizations compared to existing methods. The observed damage features sharper contrasts, higher resolution, and clearer boundaries in affected areas, significantly improving the accuracy of damage localization. Additionally, the relative accuracy of the calculated damage stress intensity factor by the virtual twin model reaches 95%. In summary, the capabilities for real-time, remote interaction and comprehensive visualization within the digital twin ecosystem significantly enhance the management efficiency of digital and intelligent pipeline IAM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".