Reliability assessment and maintenance planning of air-operated control valves used in a nuclear power plant
Bibliographic record
Abstract
Air-operated valves (AOVs) are used to control and isolate different process systems in a nuclear power plant. The performance of an AOV is adversely affected by degradation in the pneumatic actuator and other control components of the valve. Therefore, valve overhauls (OH) are performed at a fixed interval that is mainly based on the vendor’s recommendations and the experience of the plant personnel. This paper presents a probabilistic approach to assess valve reliability and uses it as a basis to plan maintenance. The paper presents a Bayesian approach to model the valve lifetime distribution and update its parameters based on the in-service data available from the plant. Using this model, the valve OH interval is determined to achieve a target reliability level over the interval. A practical case study is presented that utilizes maintenance data from a fleet of 32-level control valves connected to steam generators at a Canadian nuclear power station. The proposed approach demonstrates that the satisfactory functioning of an in-service valve provides valuable information for extending its OH interval. Thus, the proposed approach can significantly improve the efficiency of the valve maintenance program. • Bayesian reliability analysis of air-operated valves used in nuclear plants. • Use of actual valve maintenance data to build the reliability model. • A dynamic approach to update the valve overhaul interval. • Assessment of uncertainty associated with reliability and remaining useful life. • A first study of its kind with a detailed reliability/ uncertainty assessment of AOVs.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".