Dynamic risk‐based maintenance for offshore processing facility
Bibliographic record
Abstract
Processing facilities in a marine environment may not remain safe and available if they are not well maintained. Dynamic risk‐based maintenance (RBM) methodology is a tool for maintenance planning and decision making, used to enhance the safety and availability of the equipment. It also assists in identifying and prioritizing the maintenance of equipment based on the level of risk. This article discusses an advanced methodology for the design of an optimum maintenance program integrating a dynamic risk‐based approach with a maintenance optimization technique. In this study, Bayesian Network (BN) is employed to develop a new dynamic RBM methodology that is capable of using accident precursor information in order to revise the risk profile. The use of this methodology is based on its failure prediction capability which optimizes the cost of maintenance. The developed methodology is applied to a case study involving a failure of a separator system in the offshore oil and gas production platform considering marine environments. The result shows it is essential that the valve system in the separator needs to be planned for maintenance once every 25 days; however, the cooler system can be planned for repairs once only biennially. A sensitivity analysis is also conducted to study the criticality of the operating system. © 2016 American Institute of Chemical Engineers Process Saf Prog 35: 399–406, 2016
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".