Risk Based Inspection Planning for Deteriorating Pressure Vessels
Bibliographic record
Abstract
Pressure vessels are subject to deterioration processes, such as corrosion and fatigue. If left unchecked these deterioration processes can lead to failure; therefore, inspections and repairs are performed to mitigate this risk. Oil and gas facilities often have regular scheduled shutdown periods during which many components, including the pressure vessels, are disassembled, inspected, and repaired or replaced if necessary. The objective of this paper is to perform a decision analysis to determine the best course of action for an operator to follow after a pressure vessel is inspected during a shutdown period. If the pressure vessel is inspected and an unexpectedly deep corrosion defect is detected an operator has two options: schedule a repair for the next shutdown period, or perform an immediate unscheduled repair. A scheduled repair is the preferred option as it gives the decision maker lead time to accommodate the added labour and budgetary requirements. This preference is accounted for by a higher cost of immediate unscheduled repairs relative to the cost of a scheduled repair at the next shutdown. Depending on the severity of deterioration either option could present the optimal course of action. In this framework the decision that leads to the minimum expected cost is selected. A stochastic gamma process was used to model the future deterioration growth using the historical inspection data, considering the measurement error and uncertain initial wall thickness, to determine the probability of pressure vessel failure. The decision analysis framework can be used to aid decision makers in deciding when a repair or replacement action should be performed. This method can be used in real time decision making to inform the decision maker immediately post inspection. A numerical example of a corroding pressure vessel illustrates the method.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".