Developing a quantitative risk-based methodology for maintenance scheduling using Bayesian Network
Bibliographic record
Abstract
The main objective of the maintenance process is to increase equipments life while maintaining the safety and reliability of the process systems. The maintenance planning concerns identification of what and how to inspect, how often to inspect, and what maintenance actions to be taken. Even though the maintenance may be used as an effective means for controlling the degradation of systems, the procedures may also have considerable impact on the operation. It results in direct and indirect economic consequences in terms of shutdowns and unavailability of systems. Therefore, it is necessary to plan maintenance such that a balance is achieved between the expected benefit and the corresponding economic consequences implied by these activities. The objective of this research is to integrate predictive and preventive maintenance strategies in an optimal way to maintain the desired availability and safety integrity level while minimizing the maintenance intervals. The outcome of this work would help to conserve resources while maintaining overall system availability and the safety. The results showed that the risk-based methodology developed using Bayesian Network increases the reliability of the equipment and also optimizes the cost of maintenance. Application of the developed methodology is demonstrated on the maintenance of a power plant as a case study.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".